Intelligent multi-coloured lighting system design with fuzzy logic controller

UNIVERSITY OF NAIROBI SCHOOL OF ENGINEERING

DEPARTMENT OF ELECTRICAL AND INFORMATION ENGINEERING

Intelligent Multi-Coloured Lighting System

Design with Fuzzy Logic Controller

By

Paul Wambua Mutua

F56/69356/2011

A thesis submitted in partial fulfillment for the Degree of MSc. Electrical

and Electronics Engineering in the Department of Electrical and

Information Engineering in the University of Nairobi

Previous Degrees Attained:

BSc. Electrical Engineering (2nd Upper Honours), University of Nairobi, 1988

JULY 2015

Intelligent Multi-Coloured Lighting System Design with Fuzzy Logic Controller

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Declaration of Originality

NAME OF STUDENT: Paul Wambua Mutua

REGISTRATION NUMBER: F56/69356/2011

COLLEGE: Architecture and Engineering

FACULTY/ SCHOOL/ INSTITUTE: School of Engineering

DEPARTMENT: Electrical and Information Engineering

COURSE NAME: Master of Science in Electrical & Electronic

………………………………………….. Engineering

TITLE OF WORK: Intelligent Multi-Coloured Lighting System

………………………………………… Design with Fuzzy Logic Controller

1) I understand what plagiarism is and I am aware of the university policy in this regard.

2) I declare that this thesis is my original work and has not been submitted elsewhere for

examination or award of a degree. Where other people's work or my own work has been

used, this has properly been acknowledged and referenced in accordance with the

University of Nairobi's requirements.

3) I have not sought or used the services of any professional agencies to produce this work.

4) I have not allowed, and shall not allow anyone to copy my work with the intention of

passing it off as his/her own work.

5) I understand that any false claim in respect of this work shall result in disciplinary

action, in accordance with University anti- plagiarism policy.

Signature: ......................................................................................................

Date:.................................................................................................................


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Declaration

This thesis is my original work and has not been presented for a degree in any other

University.

Paul Wambua Mutua, F56/69356/2011: ………………………….Date:…………………..

Approval

This thesis has been submitted for examination with my approval as University supervisor.

Prof. Mwangi Mbuthia: …..……………………………………….Date:………………….


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Dedication

This research work is dedicated to my family: My late wife, Eunice Mwikali, for her patience

and special support. My son, Emmanuel Muthoka, for encouraging and renting me his only

laptop computer for this research work. My daughter, Rael Munyiva, for her prayers and

spiritual strength.


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Acknowledgements

I would like to take this opportunity to thank my supervisor, Prof. M. Mbuthia, for his valuable

guidance and support throughout my MSc. course and this research work. May Almighty God

bless you in plenty as you endeavor to make the university research work interesting and of

help in producing knowledge. I also want to acknowledge Dr. C. Wekesa and the other

lecturers, at the Electrical and Information Engineering department, for their personal support

and positive critic. It could have been almost impossible without your support. You were God

sent. I thank God for you.

I take this early opportunity to thank the top management of my company, KenGen, for

allowing me to think freely on engineering academics and by extension sponsoring my MSc.

studies. I would be pleased to pass my special gratitude to the Operations Director, Eng. R.

Nderitu, and the Human Resources & Administration Director, Mrs. B. Soi, for their personal

encouragement and support throughout my studies. I want to give special thanks to my

immediate senior, Eng. S. Kariuki, and the other managers at KenGen for their understanding

and support during the studies and preparation of this thesis. I would also want to thank my

office colleague, Eng. H. Ng’iela, for his positive critic and many discussions on how to

compile a good thesis.

I would also like to thank my postgraduate colleagues and all staff in the Department of

Electrical and Information Engineering, University of Nairobi, for their support and

encouragement.

I would like to thank my family, my immediate local Kalambya/Thinu community, and friends

for their encouragement and support throughout my studies and preparation of this thesis.

Finally, I take this opportunity to thank my Lord God for guarding and guiding me throughout

the studies.


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Table of Contents

Declaration of Originality ........................................................................................................................... ii

Declaration ................................................................................................................................................ iii

Dedication ................................................................................................................................................. iv

Acknowledgements .................................................................................................................................... v

Table of Contents ...................................................................................................................................... vi

List of Tables, Graphs, and Figures ............................................................................................................ ix

List of Abbreviations and Nomenclature ..................................................................................................xiii

Abstract .................................................................................................................................................... xv

CHAPTER 1 - INTRODUCTION ................................................................................................................ 1

1.1 Background of the study ............................................................................................................ 1

1.2 Statement of the Problem .......................................................................................................... 2

1.3 Objectives of the study ............................................................................................................... 3

1.4 Research Questions .................................................................................................................... 4

1.5 Motivation and Benefits of the research study .......................................................................... 4

1.6 Scope and Limitations of the Study ............................................................................................ 5

CHAPTER 2 - LITERATURE REVIEW ........................................................................................................ 6

2.1 Introduction ................................................................................................................................ 6

2.2 Light and Sight aspects ............................................................................................................... 6

2.3 Sources of Light .......................................................................................................................... 9

2.4 The Concept of Designing a Lighting System ............................................................................ 10


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2.5 Light Emitting Diodes (LEDs)..................................................................................................... 11

2.6 Lighting Control Systems .......................................................................................................... 14

2.7 Concept of Fuzzy Logic ............................................................................................................. 15

2.8 Fuzzy Logic Controllers ............................................................................................................. 19

2.9 Lighting Systems with Fuzzy Logic controls .............................................................................. 22

2.10 Summary of the Literature Review .......................................................................................... 24

2.11 Concept Design of an Intelligent Multi-Coloured Lighting system ........................................... 25

CHAPTER 3 - MATERIALS AND METHODOLOGY .................................................................................. 27

3.1 Introduction .............................................................................................................................. 27

3.2 Typical Room Representation Diagram with Lighting Control Infrastructure .......................... 27

3.3 Functional Block Diagrams for the Lighting System Model ...................................................... 30

3.4 LED Driver ................................................................................................................................. 33

3.5 PWM - Pulse Generator ............................................................................................................ 36

3.6 Signal Source Controller ........................................................................................................... 38

3.7 Light Colour Production ............................................................................................................ 40

3.8 Modeling and Simulation of the Lighting System in MATLAB Simulink ................................... 42

CHAPTER 4 - RESULTS AND DISCUSSIONS ........................................................................................... 59

4.1 Introduction .............................................................................................................................. 59

4.2 Effect of gain factor value for Rate of Change of the Indoor Light Intensity. .......................... 60

4.3 Effect of Window Shade Position Fuzzy Logic Controller Output Gain factor ......................... 64

4.4 Effect of Outdoor Light Intensity Levels ................................................................................... 69

4.5 Effect of Different Level Values of the Red, Green and Blue Colour Portions ......................... 77


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4.6 Cause and effect of absence of Colour Decoder, Movement Detector, and Room Occupancy

Signals. .................................................................................................................................................. 84

4.7 Effect of Too High, Moderate, Low and Negative Values of Outdoor Light ............................. 86

4.8 Effect of Changing Shape of the Membership Functions ......................................................... 93

4.9 Effect of Shifting Plot Position of the Mid Membership Functions. ......................................... 96

CHAPTER 5 - CONCLUSION AND RECOMMENDATIONS .................................................................... 100

5.1 Introduction ............................................................................................................................ 100

5.2 Empirical Findings ................................................................................................................... 101

5.3 Theoretical Implication ........................................................................................................... 104

5.4 Recommendations for further research works ...................................................................... 105

REFERENCES ........................................................................................................................................... 106

APPENDICES............................................................................................................................................ 109


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List of Tables, Graphs, and Figures

List of Tables

3.1: List of the actual physical implementation devices ........................................................... 32

3.2: Control rules for input colour product signal (cp) and outdoor light level (lod) ................ 50

3.3: Indoor lighting LEDs average power output control rules ................................................ 54

4.1: Constant values – Effect of Gain factor (kp) for Rate of change of indoor light levels .... 61

4.2: Constant values – Effect of gain factor (kw) for window shade position controller ......... 63

4.3: Constant values - Effects of Outdoor Light intensity levels ............................................. 71

4.4: Constant values- Effect of different signal levels of colour red, green, blue portions ...... 78

List of Graphs

4.1: Screen shots- Effect of Gain factor (kp) for Rate of change of indoor light level ............. 62

4.2: Screen shots – Effects of gain factor (kw) for window shade controller output ................ 65

4.3: Line graph – effect of the window shade controller output gain factor ............................ 68

4.4: Screen shots – Effects of Outdoor Light intensity values ................................................. 72

4.5: Line graph – effect of the outdoor light intensity level signal on other output signals ..... 74

4.6: Screen shots – Effects of sinusoidal signal for Outdoor Light intensity values ................ 74

4.7: Screen shots- Effect of different values of RGB colour portion signal levels .................. 79

4.8: Line graph – Effect of Red, Green, and Blue values on values of the white light signal . 81

4.9: Line graph – Effect of white light signal on window shade controller output .................. 81


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4.10: Line graph –Effect of Red, Green, and Blue on respective PWM generator ref. values 82

4.11: Screen shot – Effect of zero values of colour, movement and occupancy signals. ......... 85

4.12: Screen shots- Effect of too high outdoor light intensity signal values ............................ 88

4.13: Screen shots – Effect of moderate outdoor light intensity signal values ........................ 89

4.14: Screen shots – Effect of small Outdoor light intensity signal values .............................. 90

4.15: Screen shots – Effect of changing shapes of membership functions .............................. 94

4.16: Screen shots – Effect of shifting the middle membership function plots ....................... 98

List of Figures

2.1: Additive colour mixing illustration ..................................................................................... 7

2.2: Parts of an LED ................................................................................................................ 12

2.3: Combined spectral curves for blue, green and blue .......................................................... 13

2.4: Photograph of four leads RGB LED ................................................................................. 14

2.5: Cross-section of LED lamp ............................................................................................... 14

2.6: Illustration of fuzzy logic membership function ............................................................... 17

2.7: Membership function for set of tall people (Crisp set) ..................................................... 17

2.8: Membership function for set of tall people (Fuzzy set) .................................................... 18

2.9: Fuzzy logic temperature membership function ................................................................. 19

2.10: Fuzzy inference process diagram .................................................................................... 22

2.11: Concept design of multi-coloured lighting system with fuzzy logic ............................. 25

3.1: Illustration of a typical room with controlled window shades and RGB LEDs ............... 28


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3.2: Signal flow chart for controlling the lighting system ........................................................ 29

3.3: Basic scheme of the multi-coloured LED lighting system ................................................ 30

3.4: Schematic diagram for implementing the LED lighting system ....................................... 31

3.5: LED characteristic curve ................................................................................................... 33

3.6: PWM controlled LED circuit ............................................................................................ 34

3.7: Theoretical pulse wave forms ........................................................................................... 35

3.8: PWM Generator (2-level) wave forms .............................................................................. 37

3.9: PWM generator - Illustration of pulse generation ............................................................. 37

3.10: Illustration of basic principle of Fuzzy Logic Controller ................................................ 39

3.11: Additive colour mixing demonstration ........................................................................... 40

3.12: RGB LEDs’ scheme circuit diagram ............................................................................... 40

3.13: Basic scheme for producing controlled PWM for each coloured LED .......................... 41

3.14: Screen shot - assembled lighting system model in MATLAB Simulink ....................... 44

3.15: Window shade opening position functional block diagram ............................................ 45

3.16: Screen shot - blocks for window shade position fuzzy logic controller ......................... 46

3.17: Screen shot - Window shade position fuzzy logic controller Inference System ............. 46

3.18: Outdoor light level - input Membership function algorithm plot ................................... 47

3.19: Screen shot –outdoor light level – input membership function ...................................... 47

3.20: Colour product signal - input Membership function algorithm plot ............................... 48


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3.21: Screen shot – colour product signal- input Membership function .................................. 48

3.22: Window shade position - output Membership function algorithm plot .......................... 48

3.23: Screen shot - window shade position - output Membership function ............................ 49

3.24: Screen shot – “IF –THEN” control rules for the window shade position controller ...... 50

3.25: LEDs’ output light intensity control – functional block diagram ................................... 50

3.26: Screen shot - LEDs’ fuzzy logic controller signal connections ...................................... 51

3.27: Screen shot - fuzzy logic inference system for the LEDs’ output light .......................... 51

3.28: Indoor light intensity – input membership function algorithm plot ................................ 52

3.29: Screen shot - indoor light intensity - input membership function ................................... 52

3.30: Rate of change of Indoor light intensity – input membership function algorithm .......... 52

3.31: Screen shot - rate of change of the indoor light intensity – input membership function 53

3.32: LEDs’ output light intensity – output membership function algorithm plot ................... 53

3.33: Screen shot - LEDs’ output light level - output membership function ........................... 54

3.34: Screen shot - configured fuzzy rules for the LEDs’ output light controller .................... 55

3.35: Screen shot – section showing enabler switch blocks for the LEDs’ controller output . 55

3.36: Screen shot – section showing RGB colour ratio value calculation blocks .................... 56

3.37: Screen shot – section showing RGB colour-ratio-signal product for PWM generators . 57

3.38: Screen shot – section showing LEDs and the indoor light totallizer/feedback line ........ 58


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List of Abbreviations and Nomenclature

List of Abbreviations

AI - Artificial Intelligence

ANFIS - Adaptive Neurο-Fuzzy Inference System

B-LED - Blue LED

br - colour blue part ratio

CFL - Compact Fluorescence Lamp

CIE - International Commission on Illumination

CRI (Ra) - Colour Rendering Index

cd - Candela

Cp - Colour product signal

Cs - Decoded/selected colour signal

DALI - Digital Addressable Lighting Interface

ECG - Electronic Control Gear

EN - European Standard

FL - Fuzzy Logic

FIS - Fuzzy Inference System

gr - colour green part ratio

G-LED - Green LED

IEEE - Institute of Electrical and Electronics Engineers

LED - Light Emitting Diode

lm - Lumen

lx - lux

lod - Outdoor light level signal

lodr - Outdoor light illuminating the room

lk - Indoor light level signal

MF - Membership Function

Md - Movement detection signal

pr - Window shade position reference signal

PWM - Pulse Width Modulation

R-LED - Red LED

RGB LED - combined Red, Green, and Blue colour LEDs (Multi-colour LED)

rr - colour red part ratio

Ro - Room occupancy signal

Sc - Simulink Scope block

∆lk - Indoor light level rate of change


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List of Nomenclature

Colour product signal – is the product of Red, Green, and Blue colour ratios in light colour

mixing.

Decoded/selected colour signal – the summation of Red, Green, and Blue colour ratios in light

colour mixing.

Fuzzy Logic – Logic of words and not numbers. A logic set of linguistic variables.

Fuzzy Logic Controller – Digital controller based on fuzzy logic.

Fuzzy inference – the process of formulating the mapping from a given input to an output

using fuzzy logic.

Illuminance - is the quantity of light falling on a unit area of a surface in lux (lx).

Intelligent system – a system of electrical, electronic and mechanical devises, that is in a

position to sense, calculate logic and act reasonably to produce a required result.

Indoor light – illuminance in an enclosed space or room

Lumen - a measure of amount of light given by a point source (lm).

Luminance level – quantity of light emitted from a light source (e.g. LEDs) in cd/m2.

Luminous efficacy - is the ratio of luminous flux to electrical power of the light source (lm/w).

Light Intensity - quantity or brightness of light on a surface.

Lighting design – the art and craft of creating the visual environment by means of illuminating

it.

Membership Function - This is a mathematical function represented in a curve/graphical plot

where the x-axis is a range of crisp values but the y-axis is fuzzy values which always range

from 0 to 1.

Multi-coloured lighting system – a lighting system that uses RGB LEDs as one of its source

of coloured light.

Window Shade position - Opening level of a light blinding shade for a room window.


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Abstract

This thesis is about design of an intelligent energy efficient lighting system based on fuzzy

logic controller that uses multi-colour LEDs to produce light of the required luminance level

and appropriate colour in a typical room space. The lighting system incorporates automatic

controlling of a room’s window shade opening, conveniently harvesting daylight. Appropriate

room occupancy sensors were set to dim off the LEDs if there are no people in the room. A

movement sensor was also considered for dimming the LEDs if the persons in the room are

asleep. A colour sensor or decoder was included in the control system to dim off the LEDs if

the colour signal is not initiated. The colour decoder signal determine the LEDs’ output light

colour, and close the window shades if the required room’s light colour is not white. Two

Fuzzy Logic controllers were used in the system; the first one to control opening of the room’s

window shades, and the other to control the LEDs’ output luminance level.

The study was limited to simulation of the design in a MATLAB software environment.

However, a proposal of the appropriate hardware that could be used to actualize the lighting

system was also researched and a labeled block diagram for the same provided.

The appropriate devices were identified, configured and assembled into a complete operating

simulate system using a MATLAB-Simulink applications software. The designed system was

simulated in the computer MATLAB environment, and the results were observed and

monitored using the associated Simulink scopes.

The LEDs were observed (via current pulse width) to dim accordingly in response to level of

illumination in the room; the higher the outdoor light into room, the smaller the luminance from

the LEDs. If outdoor light is very low, the window shade was observed to close. Also observed

was the window shade closing when the LEDs output light colour is not white. The simulated

model was able to calculate accurately the respective primary colour-part ratios, where the

values were used in driving the RGB LEDs for production the required room lighting colour.

The designed multi-coloured LED lighting system model is intelligent in saving lighting

electric energy, and providing room illumination levels and lighting colour requirements.


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CHAPTER 1 - INTRODUCTION

1.1 Background of the study

Nowadays lighting is seen also as a way of creating a pleasant atmosphere in the interior as a

whole and as a means of providing comfortable conditions in which to live and work. It is not

there just to improve visual perception, but also to determine the emotional atmosphere: cool or

warm, businesslike or pleasant, happy or solemn [11].

Since the discovery and use of incandescent electric lamps by end of 19th century, lighting

systems have undergone many major developments. Research for more efficient lighting

system is an on-going exercise driven by the need to save energy, the international

requirements to reduce greenhouse gases, and high cost of electricity from renewable energy

sources. Lighting can account for up to 20% of a household’s yearly electricity usage, and up to

40% a year in commercial buildings [9][11].

Energy lighting efficient research could be classified in three focuses: lamp technologies,

lighting design, and lighting control methods [1]. The three focuses could be optimized by

integrating them with an appropriate artificial intelligence (AI) system.

The current generations of light emitting diodes (LEDs) are more efficient, some having

efficacies of more than 100 lm/w [16][19][20][25][33]. The use of control technology in

electric light dimming, daylight harvesting, and movement detection can greatly improve

lighting energy efficiency [8][11][19][20]. The LED characterized by mercury-free, high

efficiency, and long life cycle is expected to be the new generation of light source [1][25][33].

The new generation of multi-colour RGB (Red, Green, Blue) LEDs offer not merely another

means to form white light but a new means to form light of different colours. As more effort is

devoted to investigating this method, multi-colour LEDs should have profound influence on the

fundamental method that we use to produce and control light colour [15][16][25][33].


2

To achieve a desired quality of light for a space, various lighting control systems have been

explored and described, including use of Digital Addressable Light Interface (DALI),

Programmable Logic Controllers (PLC), digital signal controllers (DSC), and Fuzzy Logic

Controllers [1][2][13][14][18][21][23][24][28][29][31][32].

Fuzzy logic, a branch of artificial intelligent (AI) systems, is a form of algebra employing a

range of values from "false" (0) to "true" (1) that is used in making decisions with imprecise

data. Fuzzy logic is a very powerful tool for dealing quickly and efficiently with imprecision

and non-linearity issues, like light intensity level in a room, requiring decision making [6][7]

[24][26].

Energy saving has been identified as the major concern in designing any intelligent energy

efficient lighting system [1][9][30].

1.2 Statement of the Problem

The lighting industry is going through a radical transformation driven by rapid progress in

multi-colour LED lighting, information technology, and the need for sustainable and energy-

efficient solutions. Demands on lighting systems have changed considerably in recent years.

While just switching individual lights on and off used to be sufficient, the new focus is on

dynamic lighting [5][19]20].

There is need to optimize the applications of new generation of multi-colour LEDs and the

intelligent systems, which in combination with advanced digital addressable sensors and

controls, will fuel new lighting applications. Application of Artificial Intelligent (AI) software

programs in control of multi-coloured light sources is bound to become a way of life in the near

future and lead to new lighting solutions, which are not possible with conventional lighting

technology.

It is necessary to optimize utilization of natural light in both homes and work places as a

strategy to save lighting related energy costs, and at the same time improve comfort of living

and working in such places. To do this we need to monitor and adjust the amount of light

intensity in the user places. Manually, on/off switching and discrete level dimmers can be used


3

to control the amount of light provided in a space, however, there is not much timing accuracy

using this technique – and people also forget. It is therefore necessary to automate lighting

intensity control in a space.

This study was seeking for a way to automate light intensity and colour control in a space using

fuzzy logic controllers.

1.3 Objectives of the study

1.3.1 The Main Objective

The main objective of this study was to design an intelligent lighting system based on fuzzy

logic controller that uses multi-colour LEDs to produce light of the required luminance level

and appropriate colour in a room space considering energy efficiency requirements.

1.3.2 The Specific Objectives

The specific objectives of the research study were:

1. To design hardware and control algorithms for an outdoor lighting level sensor to

automatically control a room’s window shade position;

2. To design hardware and control algorithms for an indoor lighting level sensor to

intelligently adjust the LEDs’ output light to the required room’s illuminance level;

3. To design hardware and control algorithms for an occupant detector that counts the

number of persons in a room and dim off the LEDs light if the room is not occupied;

4. To design hardware and control algorithms for a movement detector that dims off

the LEDs light if a movement in the room is not detected;

5. To design a colour decoder/sensor hardware and control algorithms for determining

the multi-colour LEDs’ output light colour and open the window shade if output

light colour is white;

6. To integrate all the sensors’ subsystems into energy efficient automated intelligent

multi-coloured lighting system.


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1.4 Research Questions

The following specific questions were used as guide for the research study:

1. How can the output of an outdoor lighting level sensor be configured and processed

to automatically control a room’s window shade position?

2. How can the output of an indoor lighting level sensor be configured and processed

to intelligently control LEDs’ luminance and maintain the required room’s

illumination levels?

3. How can the output of an occupant detector that counts the number persons in a

room be configured and processed to dim off the LEDs lights if the room is not

occupied?

4. How can the output of a movement detector be configured and processed to dim off

the LEDs if a movement in the room is not detected?

5. How can the output of a colour decoder/sensor be processed to accurately determine

the multi-colour LEDs’ output light colour and at the same time close the room’s

window shades if the output light is not white?

6. Can the integrated system of addressable sensors, window shade, fuzzy logic

controllers, and multi-colour LEDs provide a typical room with light of appropriate

colour at required intensity levels and still be efficient in electrical energy usage?

1.5 Motivation and Benefits of the research study

The author believes human beings live to enjoy life to fullest levels possible. Light is one of the

major sources of enjoyment in life. It is through light that we perceive exciting beauty in our

surroundings.

Research study on how to extract greater excitements from light, and especially on how to

create dynamic intelligent automatic coloured scenes using the new generation of multi-

coloured LEDs is on high demand.


5

Energy efficiency and sustainability are major concerns in society today. Fuzzy logic

controller, whose operation is based on expert’s knowledge and human reasoning, could be the

best option for modeling an intelligent and energy efficient lighting system, which do not

require fine accuracy in illumination levels.

The knowledge gained from this research study would be beneficial to the universities and

lighting industries. This thesis would form a basis for further studies on artificial intelligent

systems energy saving and lighting colour control. This thesis would be used in improving

energy and comfort performance of modern smart buildings.

1.6 Scope and Limitations of the Study

This thesis is about use of Fuzzy Logic concept in automating opening of window shades and

energy efficiency control of RGB LEDs’ output light intensity in a typical room space. The

study was limited to simulation of the lighting system design using Simulink blocks on a

MATLAB environment.

However, a physical implementation scheme for the lighting system, using appropriate

microprocessors and addressable digital sensors, was researched and proposed for future works.


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CHAPTER 2 - LITERATURE REVIEW

2.1 Introduction

In this chapter, literature on issues concerning light has been reviewed and explained in terms

of what light is, quality of light, measurements and definitions of light, sources of light,

concepts of designing lighting systems, LEDs, and lighting control systems. Also reviewed is

the concept of Fuzzy Logic, Fuzzy logic controllers and lighting systems based on logic

controllers. A concept design for the Intelligent Multi-coloured Lighting System with Fuzzy

logic controllers is also described.

2.2 Light and Sight aspects

2.2.1 What is light?

Light is a form of energy manifesting itself as electromagnetic radiation. The human eye

responds to wavelengths between 380nM and 780nM of the spectrum. The eye interprets the

different wavelengths within this range as colours – moving from violet, indigo, blue, green,

yellow, orange, and red as wavelength increases [9][35]. Sight, therefore, is one of the most

vital sense mankind possesses and understanding of how the eye works and how the brain

responds to the visual stimuli they receive is crucial to understanding the way light impacts on

our lives [9].

We all live in a world where at some point, colour will be a part that affect us in our everyday

lives [35]. Colour in everyday life is very diverse, from knowing that a fruit is ripe to eat, to

understanding how colour can affect our moods; blue can be calming, red can make you tense

[35].

The retina in our eyes have three types of colour receptors in the form of cones. We can detect

only three of these visible colours – red, blue and green. These colours are called additive

primaries. It is these three colours that are mixed in our brain to create all of the other colours

we see [35].


7

These are called the primary colours and additive mixing of these colours will produce all other

light colours, including white. Figure 2.1 illustrates the additive colour mixing [9][35].

Figure 2.1: Additive colour mixing illustration

Where: red + green = yellow, red + blue = magenta (purplish red), green + blue = cyan (sky

blue), and red + green + blue =white

2.2.2 Quality of Light

Good quality lighting is a crucial factor affecting our ability to perform tasks at work and at

home. It embodies a combination of several criteria including lighting level, luminance

contrast, glare and spatial distribution of the light, colour and colour rendering [9].

Although white light is a mixture of colours, not all whites are the same since they depend on

their constituent colours. So a white with a higher proportion of red will appear warmer and a

white with a higher proportion of blue will appear cooler [9][11].

2.2.3 Measurements and Definitions of some lighting terms

Photometric measurement is based on photo-detectors, devices (of several types) that produce

an electric signal when exposed to light [1][9].

Photometry is the science of the measurement of light, in terms of its perceived brightness to

the human eye. In photometry, the radiant power at each wavelength is weighted by a

luminosity function that models human brightness sensitivity; weighted according to how

sensitive the human eye is to it [9].


8

The lumen is defined as amount of light given into one steradian by a point source of one

candela strength; while the candela, a base SI unit, is defined as the luminous intensity of a

source of monochromatic radiation, of frequency 540 terahertz (555nm – wavelength, in the

green, to which the human eye is most sensitive) and a radiant intensity of 1/683 watts per

steradian. Therefore 1/683 watt of 555 nanometre green light provides one lumen. The

definition tells us that 1 watt of pure green 555nm light is "worth" 683 lumens.

Luminous flux expresses the total quantity of light radiated per 1 second by a light source. The

unit of luminous flux is the lumen (lm).

Luminous intensity is defined as the flux of light emitted in a certain direction. The unit of

luminous intensity is the candela (cd).

Illuminance is the quantity of light falling on a unit area of a surface. The unit of illuminance

is lumen/m2, or lux (lx).

Luminance describes the light emitted from a unit area in a specific direction. The unit of

luminance is expressed in cd/m2 (apparent surface).

Luminous efficacy is a measure of how well a light source produces visible light. It is the ratio

of luminous flux to power (lm/w). The luminous efficacy of a source is a measure of the

efficiency with which the source provides visible light from electricity.

Colour Rendering Index (CRI also denoted as Ra) describes the effectiveness of white light

to render all the visible colours on illuminating a surface. The scale of the Ra ranges from 50-

100.


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2.3 Sources of Light

There are three main sources of light; Sun - the source of daylight, Chemicals - the source of

fire flame, and Electricity - the source of electric light.

Sun is the source of the natural daylight. The utilization of the daylight with appropriate

shading device control in buildings is useful to complement or replace the other lighting forms,

which results in significantly lower energy consumption for lighting, while maintaining the

occupants’ visual comfort [18].

Electricity is the most modern form of energy which can conveniently be converted to visible

light energy. The quantity and quality of the light produced can easily be controlled based on

the type of light source device and the average power to the light producing device.

Various types of electric light producing devices (lamps) have been developed. Incandescent

lamp is the second oldest form of electric lighting. When incandescent lamps first appeared by

the end of the 19th century, their efficacy was just 3 lm/W, which has improved to around 14

lm/W today [9]. The halogen incandescent lamp has luminous efficacy of about 5-27 lm/w at

colour rendering index of 100 Ra.

Low-pressure sodium lamp has luminous efficacy of about 100-203 lm/w with colour

rendering index less than 30Ra. High-pressure sodium lamp has luminous efficacy of about 50-

130 lm/w at colour rendering index of 10-80 Ra [9].

The compact fluorescent lamp (CFL) is basically a low-pressure mercury gas discharge lamp

with luminous efficacy of about 60-105 lm/w at colour rendering index of 60-95 Ra. High-

pressure mercury lamp has luminous efficacy of about 35-60 lm/w at colour rendering index of

40-60 Ra. Metal halide lamps have luminous efficacy of about 75-140 lm/w at colour rendering

index of 65-95 Ra. A more recent development is the ceramic metal halide lamp, which has

luminous efficacy of about 68-90 lm/w at colour rendering index of 80-95 Ra.


10

The most recent evolution in lighting is solid-state lighting based on light emitting diode (LED)

technology. LED has luminous efficacy of about 50-130 lm/w at colour rendering index of 10-

90 Ra [9] [11].

2.4 The Concept of Designing a Lighting System

A lighting system is a string of modules that are connected systematically to produce light of

the desired quantity and quality at the required space and time. Lighting design, on the other

hand, is the art and craft of creating the visual environment by means of illuminating it.

Light gives us beauty as well as vision, and the quality is often far more important to us than

the quantity. Good lighting is both a science and an art, combining knowledge of physics,

engineering, design, physiology and psychology. Nowadays lighting is seen also as a way of

creating a pleasant atmosphere in the interior as a whole and as a means of providing

comfortable conditions in which to live and work [11].

Lighting system is not there just to improve visual perception, but also to determine the

emotional atmosphere: cool or warm, businesslike or pleasant, happy or solemn. This is the

lighting designer’s task, which is achieved by creating comfortable and stimulating lighting

systems [9][11].

The EN 12464-1 standard specifies requirements for lighting systems for almost all indoor

work places and their associated areas in terms of quantity and quality of illumination [9][34].

Around the world Legislation sets targets for the reduction of greenhouse gases, reduction or

elimination in the use of hazardous substances such as mercury and lead and mandatory

recycling of used lamps [9].

Nowadays a great portion of our source of energy is from fossil fuel that will be exhausted

soon. The cost of electricity is going to increase due to the need of investments in renewable

energy supply and shortage of crude oil [9][21].

Today, energy efficiency is an essential element of every home and business. In fact, lighting

can account for up to 20% of a household’s yearly electricity usage, and up to 40% a year in


11

commercial buildings. The use of control technology, dimming, daylight equalization,

movement detection to schedule power management through control and dimming of lights can

greatly improve efficiency going forwards. Most dimmable CFLs will dim down to 10% to

30% measured light output. Early versions of dimmable LEDs on the market have the ability to

dim lower than CFLs and can reach levels as low as 5% to 15% measured light [20].

Energy lighting efficient research could be classified in three focuses: lamp technologies,

lighting design, and lighting control method [1].

2.5 Light Emitting Diodes (LEDs)

A light-emitting diode (LED) is a semiconductor light source. LEDs were initially used as

indicator lamps in many devices and are increasingly used for other lighting. Appearing as

practical electronic components in 1962, early LEDs emitted low-intensity red light, but

modern versions are available across the visible spectrum, ultraviolet, and infrared

wavelengths, with very high brightness.

When a light-emitting semiconductor diode is forward-biased (switched on), electrons are able

to recombine with electron holes within the device, releasing energy in the form of photons.

Colour of the light produced (corresponding to the energy of the photon) is determined by the

energy gap of the semiconductor. A LED is often small in area (less than 1 mm2), and

integrated optical components may be used to shape its radiation pattern.

At the heart of every white LED is a semiconductor chip made from nitride-based materials.

The chip is traditionally positioned on top of the cathode lead. Applying several volts across

this device makes the chip emit blue light. Passing the blue light through a yellow phosphor

yields white light. Modern, high-power LEDs are variants of this architecture, featuring more

complex packages for superior thermal management. Figure 2.2 is a visual illustration of LED

lighting principles [22].


12

Figure 2.2: Parts of an LED with detailed light source architecture. Illustration by Bryan Christie [22]

The efficiency of conventional InGaN based LEDs decreases as one increases current above a

given level. This phenomenon of decrease in efficiency is called droop. Solving this mystery

will enable the design of droop-busting LED architectures that will make brighter cheaper

solid-state lighting [22].

By its very nature, LEDs can only generate monochromatic colours. Giving LEDs the blues in

1994, was the key to replacing the incandescent bulb. The first high-brightness blue LED was

demonstrated by Shuji Nakamura of Nichia Corporation in 1994 and was based on InGaN

(Indium Gallium Nitrade). In September 2003, a new type of blue LED was demonstrated by

the company Cree Inc. to provide 24 mW at 20 milliamperes (mA). This produced a

commercially packaged white light giving 65 lm/W at 20 mA, becoming the brightest white

LED commercially available at the time, and more than four times as efficient as standard

incandescent lamps [22][25][33].

There are two primary ways of producing White Light-Emitting Diodes. One is to use

individual LEDs that emit the three primary colors- red, green, and blue (RGB) and then mix

Emitted Light

Molded

epoxy lens

Anode wire

Anode lead Cathode lead

Reflective cap

Proton

Electron

Hole

p- type GaN

n- type GaN

Active region

Positive terminal

Negative terminal


13

all the colors to form white light. The other way is to use a phosphor material to convert

monochromatic light from a blue or UV LED to broad-spectrum white light, much in the same

way a fluorescent light bulb works.

So in order to create white light, two or more colours need to be combined. The blue one can be

added to existing red and green LEDs to produce the impression of white light. Figure 2.3

shows combined spectral curves for blue, yellow-green, and high-brightness red at respective

radiation wavelengths.

Figure 2.3: Combined spectral curves for blue, yellow-green, and high-brightness red solid-

state semiconductor LEDs [25].

Figure 2.4 is a photograph of a single LED with four leads for three inbuilt RGB LEDs. Figure

2.5 is a cross-section of white LEDs lamp showing part of the electronic driver board.

Wavelength (nanometers)

300 400

0

500 600 700 800

1010

2010

3010

0

4010

Intensity Count

Blue

Yellow

green

Red


14

Figure 2.4: Photograph of a four leads RGB LED Figure 2.5: cross-section of LED lamp[25]

One solution for white LED light is by mixing red, green and blue (RGB) semiconductor chips

into one single LED, or, by placing separate red, green and blue LEDs very close together, and

optically mixing the emitted radiation. Hence the method is called multi-colour white LEDs

(sometimes referred to as RGB LEDs). This method is particularly interesting in many uses

because of the flexibility of mixing different colours, and, in principle, this mechanism also has

higher quantum efficiency in producing white light.

As more effort is devoted to investigating the effective methods of mixing light colours, multi-

colour LEDs should have profound influence on the fundamental method that we use to

produce and control light colour [20][25][33].

In 2012, Cree announced a white LED giving 254 lumens per watt. In December 2012 Cree

issued another press release announcing commercial availability of 200 lumens per watt LED at

room temperature [20].

With the development of high-efficiency and high-power LEDs, it has become possible to use

LEDs in lighting and illumination [22][33].

2.6 Lighting Control Systems

A lighting control system consists of a device that controls electric lighting and devices, alone

or as part of a daylight harvesting system, for a public, commercial, or residential building or

property, or the theater [8]. Lighting control systems, with an embedded processor or industrial

Heat sink

LEDs

Cover

Driver card

Screw terminal


15

computer device, usually include one or more portable or mounted keypad or touch-screen

console interfaces, and can include mobile phone operation [8][36][37]. These control

interfaces allow users the ability to remotely toggle (on-off) power to individual or groups of

lights, operate dimmers, and pre-program space lighting levels.

Intelligent lighting can be as simple as automating a single light, so that it can be controlled by

a remote control device or timer. Intelligent lighting also refers to stage lighting that has

automated or mechanical abilities beyond those of traditional, stationary illumination.

Intelligent lighting is also known as automated lighting, moving lights or moving heads [8][27].

Demands on lighting systems have changed considerably in recent years. While just switching

individual lights on and off used to be sufficient, the new focus is on dynamic lighting

[5][19]20]. The storage and retrieval of entire lighting scenes is also in demand in this context.

In order to create lighting scenes, lights have to be assigned to particular groups, but as a rule,

the end-user also wants the option of controlling the lights individually [4][5][10].

Digital Addressable Lighting Interface (DALI) is a draft standard for a digital interface for

controlling (dimmable) electronic control gear (ECG). DALI is a joint development by a

majority of the lighting industry. The aim in developing DALI was to create a uniform standard

in the lighting industry and fill the gap between previous 1-10V control lighting interface

technology and more complex bus systems [4][5][10].

2.7 Concept of Fuzzy Logic

2.7.1 Fuzzy Logic

Fuzzy logic is a branch of Artificial Intelligence (AI) developed by Lotfi Zadeh in 1965

[7][12][24]. Fuzzy logic is a superset of Boolean logic dealing with the concept of partial truth

- truth values between "completely true" and "completely false" [6][7][12]. In classic Boolean

logic, a value is either 1 (one – completely true) or 0 (zero – completely false) but cannot be

there between as it is with Fuzzy logic.

The basic concept underlying fuzzy logic is that of a linguistic variable, that is, a variable

whose values are words, such as large, small, very bright, etc, rather than numbers. Fuzzy logic


16

is all about the relative importance of precision- “how important is it to be exactly right when a

rough answer will do?” [12][24].

Fuzzy logic uses fuzzy sets to relate classes of objects with unclearly defined boundaries in

which membership is a matter of degree. Fuzzy logic is a form of algebra employing a range of

values from "true" to "false" that is used in making decisions with imprecise data. The outcome

of an operation is assigned a value between 0 and 1 corresponding to its degree of truth [6][7]

[24][26].

2.7.2 Fuzzy sets

The idea of grade of membership, which is the concept that became the backbone of fuzzy set

theory, occurred to Lotfi A. Zadeh in 1964, leading to the publication of his seminal paper on

fuzzy sets in 1965, which marked the birth of fuzzy logic technology [6][7][24].

Fuzzy sets is a class of objects with a continuum of grades of membership. Such a set is

characterised by a membership (characteristic) function which assigns to each object a grade of

membership ranging from zero to one [6]. Fuzzy sets allow partial membership which means

that an element may partially belong to more than one set. The grade of membership ranges

from 0 (no membership) to 1 (full membership), written as in equation (2-1).

μA:U→ [0,1] (2-1)

which means that the fuzzy set A belongs to the universal set U (called the universe of

discourse) defined in a specific problem.

Just as a Boolean predicate asserts that its argument definitely belongs to some subset of all

objects, a fuzzy predicate gives the degree of truth with which its argument belongs to a fuzzy

subset [6][12][24][26].


17

2.7.3 Membership Function

A membership function is a function from a universal set U to the interval [0,1]. A membership

function is a curve that defines how each point in the input space is mapped to a membership

value (or degree of membership) between 0 and 1. The input space is sometimes referred to as

“Universe of discourse” [12][24]. Figure 2.6 shows representation of the membership function -

input U is mapped to the corresponding degree of membership µ in the range of 0 to 1.

The curve known as a “membership function” is often given the designation of μ. The value μ

is the degree of membership, which is along the y-axis of the graph. The universe of discourse

U is along the x-axis of the membership function graph.

Consider, for example, the set of membership function for a set of tall people. If the set is given

the crisp boundary of a classical set such that, if more than 6ft is tall, then less than 6ft is short.

This crisp set is illustrated in figure 2.7.

Height (ft) 0

0

1

μ

Membership

function

(mapping)

Input space -U

“Universe of

discourse”

Degree of

membership- µ

- [0,1]

0.5

6

TALL

SHORT

Figure 2.7: Membership function -Set of tall people (Crisp Set)

Figure 2.6: Illustration of membership function


18

But for more realistic, a smooth curve from SHORT to TALL is a fuzzy set such that a person

may be both tall and short to some degree. This is illustrated in figure 2.8.

2.7.4 Fuzzy Logical Operations

Fuzzy logical reasoning is the fact that it is a superset of standard Boolean Logic. The set

operations intersection and union correspond to logic operations, conjunction (AND) and

disjunction (OR), respectively. In the fuzzy logic, the statement A AND B, where A and B are

limited to the range (0,1), is resolved by using the function min(A, B). Also the operation A

OR B becomes equivalent to max(A, B). The operation NOT A becomes equivalent to the

operation 1-A [6][12][24].

The set operations of intersection (AND), union (OR), and complementation (NOT), are

defined in terms of characteristic (membership) functions in the following fuzzy logic

equations (2-2), (2-3), and (2-4) respectively:

Intersection: μA∩B(x) = min(μA(x), μB(x)) (2-2)

Union: μAUB(x) = max(μA(x), μB(x)) (2-3)

Complement: μnot A(x) = 1-μA(x) (2-4)

Height (ft) 0

0

1

μ 0.5

6

SHORT TALL

Figure 2.8: Membership function - Set of tall people (Fuzzy Set)

4


19

2.8 Fuzzy Logic Controllers

A fuzzy control system is a control system based on fuzzy logic—a mathematical system that

analyzes analog input values in terms of logical variables that take on continuous values

between 0 and 1. This is in contrast to classical or digital logic, which operates on discrete

values of either 1 or 0 (true or false, respectively) [26]. Fuzzy logic is used, for example, in

artificial intelligence (AI) systems. It is a very powerful tool for dealing quickly and efficiently

with imprecision and non-linearity issues requiring decision making.

Fuzzy system is better placed in solving day-to-day problems due to its ability to be built on top

of the experience of experts and can be blended with conventional control technologies

[12][24]. Traditionally, fuzzy logic has been viewed in the Artificial Intelligence (AI)

community as an approach for managing uncertainty. In the 1990's, however, fuzzy logic has

emerged as a paradigm for approximating a functional mapping [12][24][26].

Fuzzy logic control relies on basic physical properties of the system, and it is potentially able to

extend control capability even to those operating conditions where linear control techniques

fail.

A basic application might characterize sub ranges of a continuous variable [26]. For instance,

a temperature measurement for anti-lock brakes might have several separate membership

functions, as shown in figure 2.9, defining particular temperature ranges needed to control the

brakes properly. Each function maps the same temperature value to a truth value in the 0 to 1

range. These truth values can then be used to determine how the brakes should be controlled.

Figure 2.9: Fuzzy logic temperature membership function [26]

Cold -blue

Warm-orange

Hot- red


20

In this image, figure 2.9, the expressions cold, warm, and hot are represented by functions

mapping a temperature scale. A point on that scale has three "truth values"—one for each of the

three functions. The vertical line in the image represents a particular temperature that the three

arrows (truth values) gauge. Since the red arrow points to zero, this temperature may be

interpreted as "not hot". The orange arrow (pointing at 0.2) may describe it as "slightly

warm" and the blue arrow (pointing at 0.8) "fairly cold" [26].

A fuzzy controller is based on a collection of conditional statements known as “If-Then” rules.

2.8.1 “If-Then” Fuzzy Statement Rules

The purpose of fuzzy logic is to map an input space to an output space, and the primary

mechanism for doing this is a list of “IF – THEN” statements called rules, placed between the

input space and output space.

A Fuzzy rule is a conditional statement Rk based on expert knowledge expressed in the form of

fuzzy equation (2-5):

Rk: IF x is A THEN y is B (2-5)

Where A (e.g. small) and B (e.g. large) are linguistic values defined by fuzzy sets, on the range

(universe of discourse) x and y respectively. Rk is the kth rule.

If there are n rules, the rule set, R, is represented by the union of the rules in the form of the

fuzzy equation (2-6).

R = R1 else R2 else R3 else ... Rn. (2-6)

The “IF” part of the rule “x is A” is called antecedent or premise. The “THEN” part of the

rule “y is B” is called the consequent or conclusion. In general, the input to an if-then rule is

the current value for the input variable, U (universe of discourse), and the output is an entire

fuzzy set, µ ([0, 1]).


21

Interpreting if-then rules is a three-part process: Fuzzy inputs - Resolve all fuzzy statements in

the antecedent to a degree of membership between 0 and 1. Apply fuzzy operator to multiple

part antecedents and resolve the antecedents to a single number between 0 and 1. Apply

implication method- use the degree of support for the entire rule to shape the output fuzzy set.

If the antecedent is true to some degree of membership, then the consequent is also true to the

same degree. Thus in binary logic: if p → q (here p and q are either both true or both false). In

fuzzy logic: if 0.4p → 0.4q (here partial antecedents provide partial implication). The

“implication function” then modifies that fuzzy set to the degree specified by the antecedent.

In general, one rule alone is not effective. Two or more rules that can play off one another are

needed [12].

2.8.2 Fuzzy Inference Systems

Fuzzy inference is the process of formulating the mapping from a given input to an output

using fuzzy logic [12]. There are two types of fuzzy inference systems that vary somewhat in

the way outputs are determined [12][24]: Mamdani type:- proposed in 1975 by Ebrahim

Mamdani - the output membership functions are single spike fuzzy sets (singleton outputs)

rather than distributed fuzzy sets. Mamdani method finds the centroid of the two-dimensional

output function for defuzzification purpose. Sugeno type:- used to model any inference system

in which the output membership function are either linear or constants [12].

Mamdani method Fuzzy inference process comprises of five parts: first step is fuzzification of

the input variables where the degree to which they belong to each of the appropriate fuzzy sets

via membership functions is determined. The second step is application of the fuzzy operator

(AND or OR) in the antecedent. The fuzzy operator is applied to obtain one number that

represents the result of the antecedent for that rule. The third step is implication from the

antecedent to the consequent. The rule’s weight must be determined first. The forth step is

aggregation of the consequents across the rules the fuzzy sets that represent the outputs of each

rule are combined into a single fuzzy set. The fifth step is defuzzification where a single crisp

number is determined by centroid method of the aggregate.


22

This fuzzy inference process could best be explained in a diagram as in figure 2.10 [12].

In this figure 2.10, the flow proceeds up from the inputs in the lower left, then across each row,

or rule, and then down the rule outputs to finish in the lower right.

The most popular defuzzification method is the centroid calculation, which returns the center of

area under the aggregated curve.

2.9 Lighting Systems with Fuzzy Logic controls

In a paper by S.D. Panjaitan [1], fuzzy logic scheme is proposed as a basis to control lighting

system in building zone where the fluorescent lamps are the controlled objects. The proposed

lighting design procedure determines the maximum number of lamps that should be used

according to the room function. Then the proposed controller is activated and deactivated

Output

Input 1 Input 2

1- IF

2- IF

AND

AND

THEN

THEN

Figure 2.10: Fuzzy inference process diagram

Fuzzification Fuzzification

Operator and Implication

Operator and Implication Aggregation

Defuzzification


23

according to information from occupancy sensors, which gives information about room

occupancy. The fuzzy set of the output variable is defuzzified to deliver a crisp numerical value

by the centroid-of-area method. Programming of the Fuzzy Logic Controller for the lighting

control system was realized by using Software Assembler DT51S.

Papers by [1], [22] and [29] describes the development of fuzzy logic controller for room

lighting system using a microcontroller. Input to the fuzzy logic controller is room light level

and the outdoor daylight level and the number of occupants in the room. The controllers will

control daylight shading, and the number of lamps to be switched on while maintaining the

suitable illuminance for the specific condition of the room. The paper by M.A.A. Saleh [29]

describes a fuzzy controller that controls the number of lamps lit using the number of people

inside the room and the required illuminance as control inputs.

M. T. Lah [18] proposed a modern approach to control the inside illuminance with fully

automated fuzzy system for adjusting shades, which responds constantly to the changes in the

available solar radiation, which makes decisions as it follows the human thinking process. The

control algorithm contains a cascade control with fuzzy controller as the main and conventional

PID-(proportional-integral-derivative) controller as auxiliary controller. The implementation of

fuzzy logic controller for the roller blind positioning is Sugeno type.

C. Zhang [28] developed a microprocessor-based intelligent control device for streetlight

control, applying fuzzy decision theory to distinguish accurately various interferences and to

make it operate, reliably, a transformer high voltage circuit breaker.

The paper by M. L. Jin [2] describes how we can design a lighting control system including

hardware and software based on fuzzy logic. Software incorporates LABVIEW graphical

programming language and MATLAB Fuzzy Logic Toolbox to design the light fuzzy

controller. The control system can dim the bulb automatically according to the environmental

light. The fuzzy set of the output variable is defuzzified to deliver a crisp numerical value by

the center-of-gravity method.

K. K. Tan [13], S. Mehan [14] and C. M. Prades [31] describes the implementation of an

intelligent traffic lights control system using fuzzy logic technology which has the capability of


24

mimicking human intelligence for controlling traffic lights. The papers [13][14][31] proposed

fuzzy logic controlled traffic light that uses sensors that count the numbers of cars in a queue

and extend or reduce time needed for the green light on the arrival side. The reasoning method

in the fuzzy controller is similar to that of a policeman handling the traffic flow at a typical

junction.

K. Alexandridis [23] proposed a control method that uses digital PI-like fuzzy logic controller

to improve both lighting level and energy efficiency at the same time. Hybrid systems like

ANFIS (Adaptive Neurο-Fuzzy Inference System) was used for prediction and control of the

artificial lighting in buildings, following the variations of the natural lighting. The indoor

illuminance levels together with the Daylight Glare Index are taken into account by the fuzzy

control scheme to regulate the shading and electric lighting.

M.G. Shafer [32] proposes a model of fuzzy logic control system to control illumination of

LEDs lamp, in order to obtain homogeneity of light intensity in a room.

2.10 Summary of the Literature Review

The literature described various sources of light and why quality of the light is more important

than its quantity in illuminating a space. To achieve a desired quality of light for a space,

various control models are described, including DALI and Fuzzy Logic Controllers. Electrical

energy saving has been identified as the major concern in designing any intelligent and efficient

lighting system.

However, the author noticed a need in research for an intelligent lighting system that takes

advantage of availability of daylight and the new generation of high efficient RGB LEDs in

production of both white light and multi-coloured light. The author proposed a design of a

multi-coloured lighting system, based on Fuzzy Logic, where the electric light source changes

its light intensity and colour automatically to the desires and convenience of the user.


25

2.11 Concept Design of an Intelligent Multi-Coloured Lighting system

Figure 2.11 is a block diagram illustrating the concept of a lighting system with fuzzy logic

controller. It explains the process of automating multi-coloured LEDs lighting intensity and

colour change in a room. The proposed system has five inputs and four outputs.

The input “Daylight” refers to the natural outdoor light from the sun as detected by a light

sensor: If the daylight intensity level is at the required minimum setting, the room’s sun light

window shades (blinders) automatically opens to maximum position possible. If the daylight

intensity is lower than minimum set point, the shades closes.

The input “room light intensity” refers to the lighting level in the room area or space as

measured by the installed photometer (light sensor): This determines the dimming of the LED’s

light. If the light level is higher than the set point, the LEDs automatically dim down to the

required room light level. If the light level in the room is lower than the set point, the sun

shades are opened further, but if the sun shades are fully open and the room light level is still

low, the LEDs output light is increased. If the room light level is higher than the set point and

the LEDs are fully dimmed, then the shades are automatically partially closed.

The input “Desired room light colour” refers to the user’s preferred colour as selected on a

touch pad colour chart or colour sensor or mood detector: The light produced by the LEDs will

be of the colour as the selected by the user on a colour chart touch pad or detected

INPUT

1. Outdoor light

(daylight) intensity

2. Room light intensity

3. Desired light colour

4. Number of people in

room (occupancy)

5. Movement detection

Fuzzy

Inference

System

Micro -

controller

and the

LEDs’

drivers

RGB

LEDs

OUTPUT

1. On and Off

2. Light colour

3. Dimming

4. Daylight

shades’

position

Figure 2.11: Concept design of a multi-coloured lighting system with fuzzy logic controller


26

colour/mood. If the selected colour is not white, then the room’s window outdoor light shades

will automatically close fully.

The input “room occupancy” is same as “number of people in the room” which is the

number of people occupying the room as computed by an entrance counter detector: If the

computed number of persons in the room is one or more, then the LEDs system will

automatically be activated to normal operation. If the computed number of persons is zero, then

the LEDs light will automatically be dimmed off.

The input “movement detection” refers to motion sensors detecting movement in the room: If

the motion sensors do not detect movement in the room within a predetermined duration, then

the LEDs light is dimmed off. If a movement in the room is detected, then the LEDs system is

activated to normal operations.


27

CHAPTER 3 - MATERIALS AND METHODOLOGY

3.1 Introduction

How the research was performed is briefly explained in this chapter. A line diagram

representing a typical room is described, where the indoor room illumination is the subject of

control. The general functionality of the multi-coloured lighting system is shown in a labeled

single line diagram. The functions and the principles of operation of each component in the

lighting system are also described. Also shown is schematic diagram with hardware that may

be used in implementing the lighting system.

MATLAB computer software environment is used for simulating operations of the lighting

system. Screenshot of the assembled MATLAB Simulink model of the multi-coloured lighting

system is shown. Also outlined here are the operational assumptions for the simulation.

Screenshots of the membership function plots and the “IF-THEN” statement control rules for

two Fuzzy Logic Controllers are explained.

The function and purpose of each section of the MATLAB-Simulink lighting system model is

explained with aid of sectional screenshots. The simulation processes and points for monitoring

the lighting signal levels and the LEDs’ current pulses are explained.

3.2 Typical Room Representation Diagram with Lighting Control

Infrastructure

The figure 3.1 is a labeled diagram representing a typical room whose inside illumination level

is controlled by light from a set of ceiling mounted multi-colour LED lamps and daylight

(outdoor light) through clear-glass window with electrically movable shades (outdoor light

blinders). It is assumed that the number and the rating of the multi-colour LED lamps is enough

to illuminate the whole room uniformly and to the required light intensity. It is also assumed

that the windows are large enough to allow passage of the daylight to illuminate the room

uniformly to the required light intensity. The window shades are expected to blind out the


28

outdoor light. The amount of outdoor light entering the room is expected to be proportional to

the opening of the window shades. Opening of the window shades is electrically controlled.

There is only one indoor light sensor, which is ceiling mounted at centre of the room. The

outdoor light sensor is strategically mounted to monitor the daylight levels that could pass

through the window into the room. The movement detector is mounted in front side of the room

in a position such that it can detect motion of persons in the entire room space. The room

occupancy counter is composed of two detectors that are conveniently mounted sequentially at

the door frame such that the first detector in entering is assigned addition to number in the

room, and other detector on the line, which is first in leaving the room, is assigned subtraction

to the number in the room. The colour selector/sensor/decoder module is mounted conveniently

at the entrance area of the room such that the user has an option of either select a colour by

touch of a colour-chart or the module can sense a facial mood of a person or the module can

decode clothing colour and automatically set the colour of the LED lamps. The colour signal is

expected to determine the window-shades’ opening position. Figure 3.2 is the flow chart for

signal controlling the lighting system.

Outdoor light

Outdoor light

Room window

Room window shade

Movement

detector

Room window

shade opening

position

mechanism

Number entering

counter

Number leaving

counter

Outdoor light

intensity sensor

Room

illumination

level sensor

Multi-coloured

LED lamp

Colour

sensor/decoder

Figure 3.1: Illustration of a typical room with controlled multi-coloured LED lighting

system


29

START

System power and signal

initialization

Is Outdoor light

sensor signal on?

Window Shade Position

Fuzzy Logic Controller

Is Room’s light

sensor signal on?

LEDs’ output Fuzzy

Logic Controller

Is room

occupancy ≥1?

Is movement

detector signal on?

Is colour

signal High?

Calculate ratios for

red, green and blue

colour portions

Pulse Width Modulation

(PWM) generators

Send zero value signal

to PWM generators

LED drivers

Room Illumination

levels

Yes

Yes

Yes

Yes

Yes

No

No

No

No

No

Figure 3.2: Signal flow chart for controlling the lighting system

Calculate colour

product

Motor drivers

Window shade

position


30

3.3 Functional Block Diagrams for the Lighting System Model

3.3.1 Functional Representation of the Lighting System Model

Figure 3.3 shows basic scheme of the multi-coloured lighting system with two fuzzy logic

controllers; one for controlling position of the window light-shade opening (position) and the

other for controlling the average output light intensity (luminance) of the multi-colour LEDs.

Fuzzy Logic Controller for the

window shade position

Outdoor light (lod)

sensor signal Microcontroller-

motor driver

Window Shade

Position (sp)

Feedback

Colour Decoder /

sensor

Colour product

signal (Cp)

colour red part

colour green part

colour blue part

Room

Occupancy (Ro)

signal

Movement

detector (Md)

signal

Indoor light

intensity (lk)

signal

Indoor light rate

of change (∆lk)

signal

R-LED

G-LED

B-LED

LED

Driver

LED Driver

LED

Driver

PWM

PWM

PWM Multiplier

Multiplier

Multiplier

rr

br

gr

Figure 3.3: Basic scheme of the multi-coloured LED lighting system

Fuzzy Logic Controller for the LED average output light

Signal controlled enabler

switches

Highest value

selector

Summation of

colour ratios (Cs)

Multiplier of the

colour ratios

Colour ratio

calculator

x

/

x

/

x

/


31

The multi-coloured lighting system is based on Fuzzy Logic controllers which enable it have

the necessary operational intelligence that mimic human decision making process. Daylight

(outdoor lighting) harvesting through windows with controllable window-shade mechanism

was necessary for reducing use of electrical energy. The system uses multi-colour (RGB) LEDs

for purpose of producing dynamic coloured light in a typical room space.

3.3.2 Physical Implementation schematic diagram

Figure 3.4 is a labeled schematic diagram with actual industrial electronic devices proposed for

implementing the multi-colour LEDs room lighting system. All the lighting system actual

devices are digitally addressable and are expected to intercommunicate through the I2C (Inter-

Integrated Circuit) bus. The software for the Fuzzy Logic Toolbox and the colour decoder

signal processing, would be installed in the master microcontroller.

Master

Microcontroller (PIC16F690)

Slave 1 Microcontroller

(PIC16F690) Indoor light

sensor –

(TSL29020)

Colour

sensor (TCS3472)

Motion

detector (DP-002A)

People Counter

Room occupancy

(PC6464M2)

Motor for moving

the window shade

Window Shade position

decoder (IEK58MM)

Shade position

feedback signal

+VDD

Red LED

(ASMT-YTD2-0BB02)

Green

LED

MOSFET

(2N7000)

I2C - Bus

Outdoor light

sensor –

(TSL29020)

Slave 2

Microcontroller

(PIC16F690)

Motor Driver

interface

Blue

LED

Figure 3.4: Schematic diagram for implementing the LED lighting system

LED Driver

interface


32

The various actual devices that may be used in constructing (implementing) the multi-coloured

lighting system are described in the table 3.1 for list of items.

Table 3.1: List of the actual physical devices for realizing the lighting system

Item Description Manufacturer Part No./Type Remarks

1 Master

microcontroller

Microchip Technology Inc.

USA.

http://www.microchip.com

PIC16F690 Master

Microprocessor

2 RGB LEDs AVAGO Technologies,

USA

http://www.avogotech.com

ASMT-TYD2-

0BB02

PWM driven

3 Microcontroller –

PWM generator for

the LEDs’ drivers

and the Shade

position motor drive

Microchip Technology Inc.

USA.

http://www.microchip.com

PIC16F690 Serial

addressable

device through

I2C Bus

4 -Indoor light sensor

-Outdoor light

sensor

Texas Advanced

Optoelectronic Solution

(TAOS) Inc. USA

http://www.taosinc.com

TSL29020 Digital

addressable

devices.

5 Window Shade

position decoder

TR-Eletronic GmbH,

German

http://www.tr-electronic.de

IEK-58MM

(Integrated

Coupling

Incremental

Encoder )

Digital

addressable

device

6 Colour sensor Texas Advanced

Optoelectronic Solution

(TAOS) Inc. USA

http://www.taosinc.com

TCS3472 Digital

addressable

device

7 Motion detector Glolab Corporation, USA

http://www.glolab.com

DP-002A Digital

addressable

device

8 Room Occupancy

counter (People

counter)

IEE, Luxembourg

Web: www.iee.lu

PC6464M2 Digital

addressable

device


33

3.4 LED Driver

LEDs are current driven devices. Figure 3.5 shows the characteristic curve of a LED [1]. The

y-axis is the current, in mA, through the LED while the x-axis is DC voltage, in volts, across

the LED. By increasing the voltage in the ∆V range, the current increases almost proportional.

The brightness of an LED is approximately proportional to its average current. However, a

current variation in an LED may cause colour shift. Such an approach is not appropriate for

applications, which strictly requires a consistent colour gamut [3]. For this reason a driver

circuit is designed essentially to drive LEDs at the required constant current.

To prevent the LED lighting from colour shift, the LED lamp can be dimmed by pulsed current.

The average LED current is adjusted by the duty cycle of the PWM.

Figure 3.6 shows a LED circuit with a pulse controlled switching device. The switching device

(LED driver) is fed with a controlled signal from a PWM pulse generator. The average load

current Io is a function of the duty cycle of the PWM for controlling opening and closing of the

circuit switch S. The resistor R is for limiting the current amplitude.

Figure 3.5: LED characteristic curve [3].

LED voltage in volts

LED

current

in mA


34

When switch S is closed, the current, Io, rises instantaneously to Vs/R. When switch S is

opened, the current at once falls to zero.

Figure 3.7 shows the theoretical wave form of the voltage across the associated LED resistor,

VR, and the current Io. The voltage, current and power delivered to the LED is represented by

equations (3-1) to (3-7).

A chopped dc voltage is produced in the LED resistor terminal:

VR = TON/(TON + TOFF)Vs = TON/T Vs = αVs (3-1)

Where T=TON +TOFF = chopping period.

α = TON/T = duty cycle (duty ratio)

Thus, the LED and resistor average voltage, Vo, can be controlled by varying the duty cycle α.

Average output voltage Vo = αVs (3-2)

Average output current Io = αVs /R (3-3)

PWM

PWM - pulse

generator

S - Pulse controlled

switch

LED

R- Resistor associated

with the LED circuit.

+ - DC

source

Vs

Io

Figure 3.6: PWM controlled LED circuit

VR


35

Calculation of the RMS values and the average power consumed by the LED.

RMS value of output voltage Vor = [αVs2]1/2 = √α Vs (3-4)

RMS value of output current Ior = [α(Vs/R)2]1/2 = √α (Vs/R) (3-5)

Power delivered to the LED Po = Ior Vor = √α(Vs/R) √α Vs = α (Vs2/R) (3-6)

Hence, Po = Kα (3-7)

Where K is a constant, (Vs2/R), as long as the dc source, Vs, is maintained constant.

The average power Po through the LED resistor is controlled through α (duty cycle) by opening

and closing the switch S periodically by means of the PWM pulse generator. The Po is

proportional to the LED’s luminosity level.

time

time

VR

Io

Vs /R

Vs

0

0 T

TOFF TON

time T

TOFF TON PWM

Figure 3.7: Theoretical Pulse wave forms – (a) PWM generator output, (b) voltage VR

across the resistor, (c) current Io through the LED.

0

t

(a)

0t

(b)

0t

(c)

0t


36

A means to control the duty cycle, α, of the PWM pulse generator output is all what was

required.

3.5 PWM - Pulse Generator

A Pulse Width Modulator (PWM) generator is a device which is a source of control signal of

pulses with constant amplitude and frequency but variable duty cycle.

The reference signal, called modulating signal, is naturally sampled and compared with a

symmetrical triangle carrier as shown in figure 3.8, graphical representation. A PWM generator

block, used in MATLAB Simulink computer software, has two output signal; pulse-upper

device level (high=1) and pulse-lower device level (high=1). When the reference signal is

greater than the carrier, the pulse for the upper switching device is high (1), and the pulse for

the lower device is low (0).

The triangular carrier wave is used to fix the frequency of the output pulses.

For uniformly generated rectangular output pulses, a constant input signal is used as reference.

The output pulse width (the duty cycle) is varied by varying magnitude of the input signal as

demonstrated in figure 3.8 and figure 3.9 wave forms. Part (a) of figure 3.9 shows the

triangular carrier wave form cross-cut with constant reference signals, one with value more

than zero and another with value less than zero.

Part (b) of figure 3.9 shows two sets of the generator out pulses with same period T; one with

wider TON duration as a result of the input reference signal of value more than zero (signal line

of colour blue). The other pulse has a narrow TON duration which results from an input

reference signal of value less than zero (signal line of colour red). The narrow pulses are also

shown in part (c). Width of the output pulses is proportional to the value of the input signal in

the range of -1 to +1.

An appropriate source of signal to control the duty cycle of the PWM generator was required.


37

Figure 3.8: PWM Generator (2-level) wave forms – (a) the reference and the triangular

carrier, (b) the upper device output pulses, (c) the lower device output pulses.

Figure 3.9: PWM generator - Illustration of pulse generation; (a) input reference signals, (b)

output pulses (c ) Narrower pulse with PWM generator reference input value less than zero

Period of pulse with

wider width

Period of pulse with

narrower width

(a)

(b)

0

-1

+1

0

+1

Time

Time T

TON TOFF

T

TOFF TON

Triangular carrier wave

form

Constant input signal

value more than zero

Constant input signal

value less than zero

Time 0

+1

(c )


38

3.6 Signal Source Controller

The signal level which is input to PWM generator, for variable output pulse width, requires

control. The indoor light sensor output signal, was identified as the initiator that affect the

required LEDs output. An appropriate controller was required to be between the indoor light

level sensor and the PWM pulse generator. The controller output signal level was required to be

variable values in the range of -1 to +1, as input to the PWM generator.

There are several types of controllers that would be used.

3.6.1 Conventional Controllers

The conventional controllers like PIDs, PLCs, digital signal controllers, microcontrollers,

digital signal processors were considered. Each of the conventional controllers was found to be

heavily dependent on complex software programming.

A digital signal controller (DSC) is a hybrid of microcontrollers and digital signal processors

(DSPs). Like microcontrollers, DSCs have fast interrupt responses, offer control-oriented

peripherals like PWMs and watchdog timers, and are usually programmed using the C

programming language, although they can be programmed using the device's native assembly

language. DSCs are used in a wide range of applications, but the majority goes into motor

control, power conversion, and sensor processing applications. Currently DSCs are being

marketed as green technologies for their potential to reduce power consumption in electric

motors and power supplies.

3.6.2 Fuzzy Logic controllers

Fuzzy logic controllers are fashionable in areas of artificial intelligence like in sense and

control of intelligent systems. The controllers rely on expertise knowledge and desires of the

system user. Very little programming is required. It is easier to program the controller using

the already packaged software, Fuzzy Logic Toolbox, in MATLAB computer software

environment.

http://en.wikipedia.org/wiki/Interrupt

http://en.wikipedia.org/wiki/C_(programming_language)

http://en.wikipedia.org/wiki/C_(programming_language)


39

Fuzzy Logic control system was chosen as the most appropriate controller for the lighting

system due to its simplicity, generalization of light level settings, and auto-control ability.

A basic configuration of a two input fuzzy logic controller is illustrated in figure 3.10. The U1

and U2 in the diagram represent the first and the second input signals respectively. The output

signal of the controller is a crisp value that may be used as reference (input) for the PWM pulse

generator.

The fuzzy logic controller comprises of four principal components:

1. Fuzzification Interface:- It converts input into suitable linguistic values using a

membership function.

2. Knowledge base:- Consists of a database with the necessary linguistic definitions and

the control rules.

3. Inference Engine:- It simulates a human decision making process in order to infer the

fuzzy control action from the knowledge of the control rules.

4. Defuzzificztion interface:- Converts an inferred fuzzy controller output into non-fuzzy

(crisp) control action signal.

Knowledge Base

Output

U1

U2

Input Fuzzification

interface

interface

Inference Engine

Decision Making

Logic

Defuzzification

interface

Figure 3.10: Illustration of basic principle of Fuzzy Logic Controller

Output


40

3.7 Light Colour Production

The primary colors of light are red, blue, and green. The secondary are yellow, cyan, and

magenta. When beams of light are mixed without any absorption, an additive process occurs.

The more we mix the beams, the closer they get to being white light. Figure 3.11 demonstrates

the additive colour mixing.

Figure 3.11: Additive colour mixing demonstration

The average current through each LED determines its output luminance level, which is its ratio

contribution to the total output light colour. By controlling the average current through each

colour LED, any colour could be created. This is illustrated in figure 3.12.

The three coloured LEDs were expected to be physically close and next to each other so that

their output light blends well. The average current to each colour LED is expected to be a

fraction of the highest of the three. To ensure that the average current to any two of the

coloured LEDs is a fraction of the one with highest value, a colour decoder circuit is configured

IRed

IGreen

IBlue

Common

Total

coloured

light mix

output

Figure 3.12: RGB LED scheme circuit diagram – output coloured light production


41

that first identify the signal value of the highest dominant colour, then divide each colour

portion signal with the highest value. Output of the configured colour decoder circuit is three

signals, which are ratios for each respective primary colour.

The circuit connects the common control signal, from the Fuzzy Logic controller, to three

multiplication parallel circuits, where the product signal controls duty cycle of each respective

colour PWM generator as illustrated in figure 3.13, which shows the basic scheme for

controlling the average current for each coloured LED.

The signal ‘colour product signal’ shown in figure 3.13, is product of the colour signal ratios

and was used as input to window shade position fuzzy logic controller.

The signal ‘summation of colour ratios’ shown in figure 3.13, is sum of the three colour signal

ratios and was used to operate an enabler switch in the LEDs Fuzzy logic Controller output

signal circuit.

Colour Decoder /

sensor

Colour product

signal (Cw)

colour red part

colour green part

colour blue part

Figure 3.13: Basic scheme for colour product and LEDs respective PWM generator

Highest value

selector

Summation of

colour ratios (Cs)

Multiplier of the

colour ratios

Colour ratio

calculator

x

/

x

/

x

/

Signal from Fuzzy Logic Controller

PWM Generator

Multiplier

Multiplier

Multiplier PWM Generator

PWM Generator

To red

LED

To green

LED

To blue

LED


42

3.8 Modeling and Simulation of the Lighting System in MATLAB Simulink

The lighting system software model was assembled and tested using the Simulink tools

(blocks) in MATLAB software environment. Screenshots showing the various parts of the

model are explained. Algorithms for the fuzzy logic controllers are in form of membership

function plots. The configured equivalent fuzzy membership functions are also shown with

screenshots.

3.8.1 The assembled model

A screenshot of the fully assembled model, using Simulink blocks, is shown in figure 3.14.

The following was assumed for the model:

1. Daylight contribution to the indoor light (room illumination) is directly proportional to

the window shade opening position.

2. Total sum of the three colour LEDs’ output light is the only artificial light in the room

and is directly proportional to the amount of electric lighting contributed for the room

illumination.

3. The signal for controlling opening of the window shades is directly proportional to

position of the shades and the amount of outdoor light contributing to the room

illumination. i.e.

Outdoor light to the room (Lod) = constant (kod) x window shade position control signal

(pr) . This is represented by equation (3-8).

Lod = kodpr (3-8)

4. The sum total of the LEDs output light contributing to the room’s illumination is

directly proportional to the control signal for each respective PWM generator. i.e

LEDs’ light output (LLED) = constant (kLED) x sum of the signals for the PWM

generators (SPWM). This is represented by equation (3-9).

LLED = kLEDSPWM (3-9)


43

5. Total room illumination level (indoor light intensity) is sum of the outdoor daylight and

the LEDs’ sum total output light. i.e. as represented by equation (3-10).

Room illumination = outdoor light to the room + LEDs’ light output

Room illumination (Ri) = Lod +LLED =kodpr + kLEDSPWM (3-10)

6. Indoor light intensity sensor signal, which is the input signal to the LEDs Fuzzy Logic

Controller, is the room illumination level measured one simulation cycle earlier. i.e

Indoor light intensity signal (Lk)= one simulation cycle delay of room illumination

(z-1Ri). This is represented by equation (3-11), where z-1 is unit delay in sampling.

Lk = z-1Ri = Ri-1 (3-11)

7. The signal for rate of change of the indoor illumination levels is the difference between

value of the room light intensity and the value of a one simulation time cycle delayed

room light intensity signal. i.e. as in equation (3-12).

Rate of change of room illumination (∆Lk) = Room light intensity signal value (Ri-1) –

one simulation time delayed (previous) room light intensity value (Ri-2).

∆Lk = Ri-1 - Ri-2 (3-12)

8. Outdoor light level signal results from an outdoor light intensity sensor output. In the

simulation this outdoor light signal level was manually input. A preset signal generator

was also be used as input to the window shade fuzzy logic controller.

9. The colour decoder/selector/sensor is a specially designed device which can produce

three signals; one for level value of colour red, another for level value of colour green,

and the other level value of colour blue. In the simulation, each colour portion level

value constant was manually input.

10. Both the movement detector and the room occupancy signal values were also manually

input.

Signal levels for each part on the model were monitored by means of connected scope-blocks

(Sc …) as shown in the screenshot of the assembled Simulink blocks in figure 3.14.

Figure 3.14: Screenshot of the assembled lighting system model in MATLAB Simulink

Lk

Lk

ΔLk

44


45

3.8.2 Window Shade Position Fuzzy Logic Controller - Functional Block

Figure 3.15 is a representation of the functional block for controlling the room’s window shade

opening position. Figure 3.16 is screen shot showing a section of the assembled model with simulink

blocks that are used to simulate the functions of the window shade position fuzzy logic controller.

Outdoor light

(lod)

Colour

product (cp)

Cp/lod L M B

W C O M

NW C C C

Cp Fuzzification

lod Fuzzification Decision making Defuzzification

Output - Position

reference (pr)

Pr Micro-controller Window shade

position

Feedback circuit

Figure 3.15: Window shade opening position fuzzy logic functional block


46

Figure 3.16: Simulink model screenshot- section for the window shade position fuzzy controller

The two inputs to the window shade opening (position) fuzzy logic controller are:

- Outdoor light intensity level signal

- Colour product signal – if the required LEDs output light colour is white or not white

The fuzzy inference system (FIS) was set in the MATLAB Fuzzy Logic toolbox. Figure 3.17 is a

screenshot of the configured fuzzy logic inference system for the window shade position controller.

Figure 3.17: Screenshot of configured Fuzzy Inference System for window shade position

The selected Fuzzy Logic Inference system is Mamdani, where the output is a crisp single number

based on the centroid moment of the aggregated output membership function.

Figure 3.18 shows the required membership function (algorithm plot) for fuzzification of the input lod,

outdoor light level. The input range is on scale of 0 to 10 for convenience in the test.

Window shade position

Outdoor light

Colour product

Window shade Position

Controller

Mamdani FIS


47

This membership function was configured in MATLAB Fuzzy Logic toolbox and the system

membership function is shown by screenshot in figure 3.19. The Universe of Discourse, which is the

x-axis, range from 0 signal value to 10 signal value for convenience of the tests.

Figure 3.19: Screen shot of configured input Membership Function for outdoor light level

Figure 3.20 shows the required membership function (algorithm plot) for the input colour product

signal cp. The Universe of Discourse, which is the x-axis, range from 0 signal value to 1 signal value,

convenient for the light colour product range. For convenience the, light colour is considered white if

the colour parts ratio product is between 0.85 and 1. Light colour is considered as either white or not

white. Figure 3.21 is screenshot of the configured membership function.

Low Mid Bright 1

0.5

0 0.0 5 10 2.5 7.5

Outdoor light intensity (lod)

μ

Figure 3.18: Outdoor light level input – Membership function algorithm plot

Low Mid Bright

Outdoor light intensity (lod) 10 0

0

0.5

1


48

Figure 3.21: Screen shot of configured input Membership Function for Colour product signal

Figure 3.22 shows the controller output membership function algorithm plot for defuzzification of the

output, Pr, for window shade position. The output position reference scale is 0 to 1, with 0 standing

for fully closed and 1 for fully open window shade. Figure 3.23 is screenshot showing output

membership function of the window shade position as was configured in the Fuzzy Logic toolbox.

1

0.5

0 0.0 0.5 1.0

White

1

0.5

0.0 0.0

Closed Mid Open

0.5 1.0

Input colour ratio product number (Cp)

Window shade position reference (pr)

μ

μ

Figure 3.20: White light signal – Membership function algorithm plot

Figure 3.22: Window shade position output – Membership function algorithm plot

Not White

0.9

Not White

Input colour ratio product number (Cp)

White

1 0 0

0.5

1


49

Figure 3.23: Screenshot of configured output Membership Function for window shade position

The operation of the fuzzy logic controller is based on a set of decision making control rules. The

fuzzy logic rules for the window shade position controller consists of a collection of IF-THEN rules

of the form

Rk: IF x1 is F1k and x2 is F2

k THEN y is Gk (3-13)

For k =1, 2, 3… n.

Where:-

- Rk is the kth rule;

- x1, x2 are members of U, for example, and y is member of V, and are the input and

output of the fuzzy logic system, respectively;

- F1k, F2

k and Gk are labels of fuzzy sets in U1, U2 and V representing the kth antecedent

pairs and consequent pair respectively;

- n is the number of rules.

Table 3.2 shows a matrix of rules (inference engine) for controlling the room’s window shade

position. The input, colour ratio product cp, is the rows’ side label, while input outdoor light level Lod,

is the columns’ top label. There are six rules for this particular fuzzy inference subsystem.

Figure 3.24 is screenshot of the rules used in the fuzzy logic controller, as configured in the Fuzzy

logic toolbox.

Closed Mid Open

Window shade position reference (pr) 0 1

1

0.5

0


50

Table 3.2: Control rules for input colour ratio product signal (cp) and outdoor light level (lod).

IF Outdoor light (lod ) IS

AND Colour product (cp) IS Low Mid High

White THEN shade is

Closed

THEN shade is

Open

THEN shade is

Mid open

Not White THEN shade is

Closed

THEN shade is

Closed

THEN shade is

Closed

Figure 3.24: Screen shot of configured “IF –THEN” rules for the window shade position controller

3.8.3 LEDs’ output light intensity Fuzzy Logic Controller – Functional block

This module consists of two interdependent inputs; the indoor light intensity level signal (Lk) and rate

of change of the indoor light level (∆Lk), which is extracted from the sensed indoor light level signal.

Figure 3.25 is a representation of the fuzzy logic controller functional block for controlling the RGB

LED output light intensity. The purpose of introducing the rate change of the light level in the room

was to stabilize signal oscillations that would occur when control system tries to increase or decrease

the room illumination levels.

Indoor

Light

Light sensor

Lk

Lk

Lk

Lk

Lk-1

∆Lk

Kp

Controller output

FLED

Figure 3.25: LEDs’ output light intensity fuzzy logic controller – functional block

Fuzzy Logic Controller for the

LEDs output average power

Unit time

delay

+

-


51

Lk is the indoor light level measured with a photometer at the kth simulation sampling instant. ∆Lk is

the rate of change of the indoor light level at the kth sampling instant. kp is a gain factor which

determines the sensitivity of the controller to the changes in indoor light level/intensity. Z-1 indicates

a unit time delay. Figure 3.26 is a screenshot showing the Simulink blocks that form the LEDs’ fuzzy

logic controller signal connections.

Figure 3.26: Screenshot showing section of assembled model for LEDs’ fuzzy logic controller

The inputs to the LEDs’ fuzzy logic controller are monitored using the indoor light intensity (room

illumination level) feed-back scope, Lk, and rate of change of room light intensity scope, ΔLk. The

output of the fuzzy logic controller was monitored through two scopes: one immediately at the

controller out by the scope block Sc-LEDs FLC output signal, and another after the room occupancy,

movement detector, and the colour select enabler switches, Sc-LEDs FLC output signal 2.

Figure 3.27 is a screenshot of the fuzzy logic inference system for the LEDs output controller

showing the two inputs and one output.

Figure 3.27: Screen shot of the configured fuzzy inference system for the LEDs’ output controller

Lk

ΔLk

Lk

Indoor light

Rate of Change of Indoor light

LEDs output Fuzzy Logic Inference

Mamdani System

LEDs Output signal


52

Figure 3.28 is the membership function algorithm plot for the room light intensity level where the

universe of discourse ranges from 0 to 10. Figure 3.29 is screenshot of the configured indoor light

intensity membership function.

Figure 3.29: Screenshot of configured input Membership Function for indoor light intensity.

Figure 3.30 is the membership function algorithm plot for the rate of change of the indoor light

intensity. The universe of discourse ranges from -1 to 1. The configured membership function is

shown by screenshot in figure 3.31.

1

0.5

0 0

Very Dim Dim Mid Bright Very Bright

2.5 5 7.5 10

1

0.5

0 -1.0 0 1.0

Positive Negative

0.5 -0.5

μ

Rate of change indoor light intensity (kp ΔLk)

μ

Indoor light intensity (illumination) level

Figure 3.28: Indoor light intensity – input membership function algorithm plot

Figure 3.30: Rate of change of Indoor light intensity – membership function algorithm plot

Indoor room light intensity 10 0

0.5

1 Very Dim Dim Mid Bright Very Bright

0

Zero


53

Figure 3.31: Screenshot of configured input Membership Function for rate of change of indoor light

Figure 3.32 is the membership function algorithm plot for output of the LEDs’ fuzzy logic controller.

The universe of discourse range was from 0 to 2. This range was set for convenience of allowing

multiplication of the respective colour ratios, the convert the product output to a range of -1 to +1.

The available simulink PWM generator allowed input of -1 to +1 hence the LEDs’ controller output

was subtracted a 1 constant value, after colour ratio multiplication.

Figure 3.33 is the screenshot showing the configured output membership function in the MATLAB-

Fuzzy logic Toolbox.

0 0

0.5

1

Max Low Low Mid High Max High

0.5 1.0 1.5 2

Proportional value of LEDs’ output light level (FLED)

μ

Figure 3.32: LEDs’ output light intensity – output membership function algorithm

(Negative) (Zero) (Positive)

Rate of change indoor light intensity (kp ΔLk)

-1 +1 0

1

0.5

0


54

Figure 3.33: Screen shot of configured output Membership Function for LEDs’ light level

Table 3.3 shows a matrix of rules for controlling the LEDs average power output using the indoor

light level Lk and its rate of change ∆Lk. The input, light level Lk, is the rows’ side label while input

light level rate of change ∆Lk, is the columns’ top labels. There are 15 rules for this fuzzy inference

subsystem. The fuzzy rule in the table 3.3 consists of a collection of fuzzy IF-THEN rules of the

form:

Rx: IF Lk is FLkx and ∆Lk is F∆Lk

x THEN LEDs’ output light level is Gx. (3-14)

for x =1, 2, 3… n.

Where Rx is the xth fuzzy rule, and n is the number of rules. FLkx is the indoor light level fuzzy set,

and F∆Lk x is the indoor light level rate of change fuzzy set for the xth fuzzy rule. Gx is the expected

LEDs’ output light level for the xth fuzzy rule. Figure 3.34 is screen shot of the configured fuzzy

rules.

Table 3.3: Indoor lighting LEDs average power output control rules (decision making table)

AND Rate of change (∆Lk) IS

IF Room light (Lk ) IS Negative Zero Positive

Very Dim THEN LED output is

Max High

THEN LED output is

Max High

THEN LED output is

High

Dim THEN LED output is

Max High

THEN LED output is

High

THEN LED output is

Mid

Mid THEN LED output is

High

THEN LED output is

Mid

THEN LED output is

Low

Bright THEN LED output is

Mid

THEN LED output is

Low

THEN LED output is

Max Low

Very Bright THEN LED output is

Low

THEN LED output is

Max Low

THEN LED output is

Max Low

(LEDs’ output light level (FLED))

(Max Low) (Low) (Mid) (High) (Max High)

0

2

0

0.5

1


55

Figure 3.34: Screenshot of the configured “IF-THEN” fuzzy rules for the LEDs’ output controller

3.8.4 Room Occupancy, Movement detector and Colour Decoder Signals – Enabler Switches

The following three signals were configured to allow or stop the LEDs fuzzy controller output signal:

- Room occupancy signal

- Movement detector signal

- Colour signal

Each of the signals was assigned an enabler switch. The enabler switches were connected in series, as

shown by the screenshot in figure 3.35. Each enabler switch would allow passage of the LEDs’ fuzzy

logic controller output signal only when the switch control signal value is 0.6 and above.

Figure 3.35: Screenshot of a section of the assembled model showing enabler switches.


56

As shown by screenshot in figure 3.36, a fraction (ratio) value for each colour is first calculated and

the ratio value used is to determine if the LEDs’ output light would be white or not; if their product

output is 0.85 to 1, then the LEDs light colour is fully white. The sum of the colour ratios was used

for the colour signal enabler switch. The screen shot in figure 3.37 show that the colour fractions

(ratios) are used individually as a multiplier of the LEDs’ fuzzy controller output signal and

determine the respective LED’s PWM generation output duty cycle and hence the LED luminance

level.

Figure 3.36: Screenshot of a section of assembled model showing colour ratio calculations

The selected SIMULINK PWM generator required an input signal which ranges from -1 to +1. But a

signal with negative value, from the fuzzy logic controller, could not be multiplied with a fraction

number representing the colour intensity. To avoid this, the LEDs fuzzy logic controller output signal

level was set to range from 0 to 2. This signal range would be converted to the required -1 to +1

range by introducing a value of -1 to the final signal.

The controller’s output signal is first multiplied by each colour portion, at their respective connection

points, then a constant value of 1 is subtracted (signal off-set) from product signal of the

multiplication. This process is illustrated in screenshot of the control signal blocks in figure 3.37.


57

Figure 3.37: Screenshot of section of assembled model showing the respective colour ratio signals

Each signal for the respective PWM generator represents the ratio composition of the corresponding

colour.

3.8.5 LEDs’ Driver – Controller Functional Block

The LEDs’ driver is represented by a signal controlled switch for each respective colour, as shown by

the screenshot section in figure 3.38. The switch in each LED circuit opens and closes in rhythm with

the pulses from the respective colour PWM generator. The resistor in each LED circuit limits the

peak current.

Also shown in the figure 3.38 is the indoor light intensity (room illumination level) feedback circuit.

The room illumination level feedback signal, which stands in for the indoor light sensor, is a sum of

the outdoor light and the LEDs’ output light.

.

Figure 3.38: Screenshot of section of assembled model showing LEDs’ driver and the indoor light intensity level (Lk) feedback line

Lk

58


59

CHAPTER 4 - RESULTS AND DISCUSSIONS

4.1 Introduction

The purpose of this chapter is to simulate and confirm whether the model and control algorithms

based on the fuzzy logic can be used to meet the objectives and answer the research questions set out

in chapter 1 sections1.3 and 1.4.

Inputs to the model were in form of constant values based on Simulink constant-blocks that are

specific for outdoor light intensity, red colour, green colour, blue colour, movement detector, number

entering room, and number leaving room respectively. A sinusoidal signal generator was also used as

input for the outdoor light sensor.

Throughout the experiment, the simulation duration was set at 0.005 seconds.

Outputs of the model were monitored by means of Simulink scope-blocks. For each specific input

value and simulation time, the corresponding model signal level was monitored for the window shade

position fuzzy logic controller output, the summed up indoor light intensity (indoor light intensity

feedback), the rate of change of the indoor light intensity, the LEDs fuzzy logic controller output, the

light colour ratio product, and the LEDs’ currents.

Effects of various gain factor values for the rate of change of indoor light intensity signal, the

window shade position fuzzy logic controller output signal, and the LEDs control sum-signal were

checked and the optimum performing values fixed for the model investigation tests.

The following investigation tests were carried out:

1. Effect of gain factor values for rate of change of indoor light intensity on stability of the

indoor (room) illumination levels.

2. Effect of gain factor values for the window shade position fuzzy logic controller output on

indoor illumination levels.

3. Effect of outdoor light intensity on indoor illumination levels and the LEDs’ output.

4. Effect of different values of the Red, Green and Blue colour portions on indoor illumination

levels and the respective LED’s current pulse width.


60

5. Effect of zero value for the colour decoder, the movement detector, and room occupancy

signals on indoor illumination levels and the LEDs’ fuzzy logic controller output signal.

6. Effect of too high and negative values of outdoor light intensity signal on window shade

controller output signal, indoor illumination levels, and the LEDs’ fuzzy logic controller

output signal.

7. Effect of changing the shape of the fuzzy logic controllers’ membership function plots.

8. Effect of shifting the mid membership function of the Fuzzy Logic controllers from the

middle position to the right hand-side of the plot.

Each simulation test outputs were displayed on computer screen. Screenshots were taken and the

display waveforms pasted on table-forms for comparison purposes. For some investigations,

simulation model signal output values were tabulated and the results displayed in form of line graphs.

4.2 Effect of gain factor value for Rate of Change of the Indoor Light Intensity.

The objective of this test was to check how values of rate of change of the indoor light intensity (∆Lk)

affect the oscillations of the indoor light intensity (Lk) and which value has the best system stability.

This refers to figure 3.26, in chapter 3, for the model’s LEDs light output fuzzy logic controller.

Based on the LEDs output fuzzy logic controller input membership functions (figures 3.29 and 3.31),

and control rules (table 3.3), the lighting system was expected to be most stable at gain factor value

of 1.

4.2.1 Results on testing effect of gain factor value for rate of change of the indoor light

intensity on stability of the lighting system simulation model

In this investigation test, constant values of the outdoor light signal, equal values for the colour

portions, and enabling signals for the room movement detector and the room occupancy are

maintained. The simulation model input data had constant values as shown in table 4.1. Graph set 4.1

compares the screenshot waveforms and their settlement values for indoor illumination level

feedback signal, rate of change of indoor illumination levels, and output of the LED light intensity

fuzzy logic controller at various values of gain factor for the rate of change of indoor illumination

levels.


61

Table 4.1: Constant values – Effect of Gain factor (kp) for Rate of change of indoor light levels

Outdoor

light level

Red

colour

part

value

Green

colour

part

value

Blue

colour

part

value

Movement

detector

value

Number

entering

room

Number

leaving

room

Window

shade

position

gain

LED

light

control

sum

gain

5 1 1 1 1 2 1 10 1

The results were obtained from the time based graphical representation of the simulation on three

scopes of the MATLAB Simulink simulation model. The results are presented by screenshots and

displayed in table format for comparison purposes as shown in graphs 4.1.

At gain factor values of 10, 0.5, and 0.3 the lighting system does not settle. But at gain value of 1,

0.292, 0.2, 0.05 and 0 the lighting system settles down; about 9 for the indoor illumination level

value, about 0.3 for the LEDs’ fuzzy logic controller output signal and 0.0 (zero) for the rate of

change of indoor light intensity. However, the system settles fastest at gain value of 0.2.

The waveforms in graph sets 4.1 show that the gain factor value on output of the rate of change of the

indoor light intensity affects the stability of the lighting control system.


62

Graph set 4.1: Set of screenshots of respective scopes - Effect of Gain factor (kp) for Rate of change of indoor light level – Comparing

graphical shapes and values

Rate of change of indoor

light level gain at 10




light level gain at 0.5



Indoor light

level

feedback

signal

Rate of

change of

indoor illumination

levels

Output of

the LED

light

intensity

fuzzy logic

controller

0 0

1.0mSec 1.0mSec 1.0mSec 1.0mSec

10

0

0 0 0

-2.0

0

7

0.4

1.8

0

0

10

-1.0

0

2.5

0.4

1.8

10

0

0

1.0

1.8

-4.0

14

-0.4

0

1.4

0

10

0.2

1.0

1.8

62

.


63

Rate of change of indoor light

level gain at 0.292







Indoor light

level

feedback

signal

Rate of

change of

indoor illumination

levels

Output of

the LED

light

intensity

fuzzy logic

controller

10 10 10 10

0 0 0 1.0 mSec 1.0 mSec 1.0 mSec

0 0

-1.0

0 0 0 0

2.5

-1.0

0.2

1.8

1.0

1.0 mSec

1.0 mSec

1.4 mSec

0

0

0

0.3

0.7

-1.0

3.5

-0.2

0.7

0.2

1.8

0

0.8

0.2

1.8

63

.

1.0 mSec


64

4.2.2 Discussion on effect of gain factor value for rate of change of indoor light intensity on

stability of the lighting system

The investigation test results show that the system stability does not depend on magnitude of the rate

of change signal but on some critical values of the rate of change. The system is seen to be stable

even without input from the rate of change of the indoor illumination levels.

The system was expected to be most stable at gain value of 1, which the simulation results show not

be so another value, 0.2, which is almost zero.

The simulation results show that the lighting system is even more stable without the rate of change of

the indoor light intensity.

4.3 Effect of Window Shade Position Fuzzy Logic Controller Output Gain factor

The objective of this test was to check how the values for the model’s window shade position

controller output signal gain factor affect the overall indoor light intensity (Lk) and which gain factor

value is most suitable and realistic for the model. This refers to figure 3.38, section of the assembled

model showing LEDs’ driver and the indoor light intensity level (Lk) feedback line.

Based on the window shade position fuzzy logic controller input membership functions (figures 3.19

and 3.21), the output membership function (figure 3.23), and control rules (table 3.1), the model

window shade opening was expected to range from 0 to 10 at a gain factor value of 10. Gain factor

values above 10 would make the indoor illumination level (Lk) for input to the LED’s fuzzy

controller to be outside the range, as per the membership function in figure 3.29. Gain values of more

than 10 were expected to make the LEDs controller output signal insensitive to the input.

It was also expected that at window shade gain factor value of zero, the indoor illumination level (Lk)

would be composed of the LEDs signal only, and the Lk signal value would be 6 (which is 3 times the

set maximum LEDs fuzzy logic controller output signal value of 2 as per the model section shown in

figure 3.37 the LEDs light sum).


65

4.3.1 Results on testing effect of the window shade fuzzy logic controller output gain factor on

indoor illumination level feedback signal and the LEDs’ output

In this investigation test, constant values of the outdoor light signal, equal values for the colour

portions, and enabling signals for the room movement detector and the room occupancy were

maintained. The simulation model input data had zero value for the rate of change of the indoor light

intensity as shown in table 4.2. Graph set 4.2 compares the shapes and values of measured indoor

illumination level feedback signal, and output of the LEDs fuzzy logic controller for various values

of gain factor on the output of the window shade position fuzzy logic controller.

Table 4.2: Constant values – Effect of gain factor (kw) for window shade position controller output.

Outdoor

light

level

value

Red

colour

part

value

Green

colour

part

value

Blue

colour

part

value

Movement

detector

value

Number

entering

room

Number

leaving

room

Rate of

change of

indoor

light

intensity

gain value

LED

light

control

sum gain

value

5 1 1 1 1 2 1 0 1


scopes of the MATLAB Simulink simulation model. The results are presented by screenshots

displayed in table format for column-wise comparison purposes. The results are also shown in line

graph 4.3, where the y-axis represents values of the indoor light intensity, the LEDs fuzzy logic

controller output, and the window shade position fuzzy logic controller output gain factor. The x-axis

of the line graph represents the gain factor value for the window shade position fuzzy logic controller

output.


66

Graph set 4.2: Set of screen shots of respective scopes – Effects of gain factor (kw) for window shade controller output.

Window Shade Position Fuzzy

logic controller output gain

factor of 0


Fuzzy logic controller

output gain factor of 3.76



output gain factor of 5



output gain factor of 7.5

Indoor light

level

feedback

signal

Output of

the LED

light

intensity

fuzzy logic

controller

0 0 0 0


0.8 0.4 0.0 0.0

0.0 0.0 0.0 0.0

1.2

1.8

4

2

6

6

8

2

4

0.8

1.8

9

6

0.6

1.8

1.0

1.0

1.0

1.0

1.8

2

8

12

4

0.4

66

.


67

Window Shade Position Fuzzy

logic controller output gain at

10



output gain at 10.5



output gain at 11



output gain at 20

Indoor light

level

feedback

signal

Output of

the LED

light

intensity

fuzzy logic

controller

0 0 1.0 mSec 1.0 mSec 1.0 mSec 0 0 0 1.0 mSec

0.2

1.8

1

0

10

14

4

1.0

1.8

1.5

0

4

10

14

0

5

10

15

0.2

1

1.8

1.0

1.5

1.8

5

0

10

20

67

.


68

Graph 4.3 is line graph showing the relationship between the window shade position fuzzy logic

controller output gain factor value and the indoor light intensity values, and the LEDs fuzzy logic

controller output values.

Graph 4.3: Line graph – effect of the window shade controller output gain factor.

From graph set 4.2, the shapes of the plots for each monitoring point are similar for each window

shade controller output gain factor. It was also noted that the indoor illumination level values increase

with increase of the window shade controller output gain factor. At gain factor value of 0, the indoor

illumination level value first shoots up to a value of about 5.5 then settles down to 3.7, the lowest

stable value, while the LEDs fuzzy logic controller output shoots up to about 1.8 then settles to a

value of about 1.26, which is the highest stable value. At window shade gain factor value of 10, the

indoor illumination level value is about 9.5, with some noticeable oscillations, while the LEDs fuzzy

logic controller output was oscillating at about 0.25. At window shade gain factor value of 10.5, the

indoor illumination level value is about 10, while the LEDs fuzzy logic controller output value was

about 0.16, the lowest attained value. At window shade gain factor value of 11.5, the indoor

illumination level value is about 13, while the LEDs fuzzy logic controller output value was 1, a

value which was not changing even with other higher values of the window shade gain factors.

The line graph in graph 4.3, also shows that the indoor illumination level values increases with

increase of the window shade gain factor, but the LEDs’ controller output value decreases to lowest


69

level of about 0.16, then rises to value of 1 for all indoor light intensity values beyond 10.5 and

window shade gain factor values above 11.49.

4.3.2 Discussion on effect of the room’s window shade position fuzzy logic controller output

gain factor

All the objectives for this investigation test were fully met. The most appropriate window shade

position fuzzy logic controller output gain factor value was confirmed to be 10. The window shade

fuzzy logic controller, which is of approximation type, output range from 0.1 to 0.9 instead of 0 to 10

for exact conventional controllers. For the closed window shade, the contribution to the indoor light

intensity (Lk) was about 1 instead of zero value. And the maximum outdoor light contribution for the

Lk was about 9 instead of the exact value of 10 for the fully open window shade position.

At window shade gain factor value of 0, window’s outdoor light contribution to Lk was also 0. The

LEDs’ contribution to Lk, which was expected to have maximum value of 6, was noted to be lower at

about 5.5 at the first oscillation peak value. As per the LEDs fuzzy logic control rules in table 3.3, an

input value of 5.5 would be in the membership function set of “mid”, and the expected controller’s

output signal value would be in set of “high” whose output signal value is about 1.5. This LEDs

fuzzy logic controller output (1.5x3 representing the Lk) would be computed to the settled Lk value of

3.7 and LEDs Fuzzy logic controller output value of 1.3. This means the required room illumination

level is achieved when LEDs fuzzy logic controller output signal is at the membership function set of

“mid” level.

This investigation tests confirmed that window shade gain factor values above 10 affects sensitivity

of the LEDs’ fuzzy logic controller. For all indoor (room) light intensity (Lk) with values above 10.5,

the LEDs fuzzy logic controller output signal would be faulty and cannot be relied on because it

remains invariable at value level of 1.

4.4 Effect of Outdoor Light Intensity Levels

The objective of this test was to check how various values of the outdoor light intensity affect the

model’s window shade position fuzzy logic controller output signal, the overall room illumination

levels (indoor light intensity Lk), and the LEDs fuzzy logic controller output signals. This was in


70

reference to the system model design shown in figure 3.14 and the respective fuzzy logic controller

membership functions in chapter 3.

In this test, the outputs of the window shade position fuzzy logic controller were expected to follow

the “if-then” control rules in table 3.1. The outdoor light intensity values were varied from 0 to 10.

The window shade position fuzzy controller output values were expected to range from 0 to 0.3 for

closed window shade position, from 0.8 to 1 for fully open window shade position, and from 0.4 to

0.7 for mid open window shade position.

4.4.1 Results of testing effect of outdoor light intensity level on indoor illumination levels and

the LEDs’ output.

The test was carried out at constant value of the window shade fuzzy logic controller output gain

factor, zero value for rate of change of the indoor light intensity, equal level values of the colour

portions, enabled room movement detector, and enabled room occupancy. The simulation model data

points were of values as shown in table 4.3. Graph set 4.4 compares the shapes and values of

measured indoor illumination level feedback signal, and output of the LED light intensity fuzzy logic

controller for various values of the outdoor light intensity signal. The test results are also shown in

line graph 4.5, where the values of the outdoor light intensity are compared with the resulting values

of the window shade position fuzzy logic controller output, the indoor illumination levels, and the

LEDs’ fuzzy logic controller output.

A signal generator with sinusoidal output was also used as the outdoor light intensity signal. The

outdoor light intensity signal wave form and the resulting wave forms of the window shade controller

output, the indoor illumination level, and the LEDs’ controller output signals are tabulated in graph

set 4.6.


scopes of the MATLAB Simulink simulation model. The results are in wave forms of the various

signals at specific monitoring points using the model scopes. The results are presented in form of

scopes’ screenshots and same displayed in table format for column-wise comparison purposes. The

results are further displayed in line graph 4.5, where the y-axis represents values of the indoor light

intensity, the LEDs fuzzy logic controller output, and the window shade position fuzzy logic


71

controller output gain factor. The x-axis of the line graph represents the values of the outdoor light

intensity. Graph set 4.6 displays wave forms of output signal where a 100Hz sinusoidal varying

signal, with amplitude of 10, was used for the outdoor light intensity level values.

Table 4.3: Constant values on effects of Outdoor Light intensity levels

Rate of

change of

the indoor

light level

gain factor

Red

colour

part

value

Green

colour

part

value

Blue

colour

part

value

Movement

detector

value

Number

entering

room

Number

leaving

room

Window

shade

position

gain factor

LED

light

control

sum

gain

factor

0 1 1 1 1 2 1 10 1

Graph set 4.4 and graph set 4.6, together with the line graph 4.5, show that the indoor illumination

level values increase with increase of the outdoor light intensity but following the window shade

opening position fuzzy logic controller rules. At very low outdoor light intensity levels, less than

value 2, the window shade opening signal is also low, at about 0.18. At middle levels of the outdoor

light intensity, about 4 to 6 values, the window shade opening signal is at maximum, about 0.9. At

high levels of the outdoor light intensity, about 8 to 10 values, the window shade opening signal is at

middle position, about 0.7.


72

Graph set 4.4: Screenshots – Effects of Outdoor Light intensity values- Comparing graphical shapes and values

Outdoor Light signal level of 0 Outdoor Light signal level

of 1

Outdoor Light signal level

of 2.5


of 5

Indoor light

level

feedback

signal

Output of

the LED

light

intensity

fuzzy logic

controller

Red LED

current

pulses

0 0 0 0

0.6 0.6 0.2 0.2


0.7 0.7 0.7 0.7

1.0 1.0 1.0 1.0

1.8 1.8

4

0 0

6

1.0

1.8

4

6

1.0

1.8

0

6

9

0.8

1.0

0

10

5

1.0

72

.


73

Graph set 4.4 – Cont…

Outdoor Light signal level of

7.5


of 10


of 11


of 20

Indoor light

level

feedback

signal

Output of

the LED

light

intensity

fuzzy logic

controller

Red LED

current

pulses

0 0 0 0

0 0 0 0 1.0mSec 1.0mSec 1.0mSec 1.0mSec

1.0

0.7

0.8

10 10 10 10

6

0.0

1.8

1.0

0.6

6 6 6

0.0

1.0

1.8

0.6

0.0

1.0

1.8

0.6

0.0

0.6

1.8

1.0

73

.


74

Graph 4.5: Line graph – effect of the outdoor light intensity level signal on other output signals.

Graph set 4.6: Set of screenshots – Effects of sinusoidal signal for Outdoor Light intensity values-

Wave forms

Signal Wave form of signal outputs

Input signal

as outdoor

light level

Sin signal,

100Hz,

amplitude

of 10.

10

5

0 0 2.5mSec 5


75

Window

shade

opening

fuzzy logic

controller

output

signal

Indoor

lighting

level

feedback

signal

LED

average

power

fuzzy logic

controller

output.

5 2.5 mSec. 0 0

2

1

0

10

6

0.1

0.9

0.5

2.5 mSec.

2.5 mSec.


76

4.4.2 Discussion on effect of outdoor light intensity level on indoor illumination levels and the

LEDs’ output.

The objective of checking how various values of the outdoor light intensity affect the model’s

functions was successful. The window shade position fuzzy logic controller output signal behaved as

expected. Graph 4.6 show that window controller output signal smoothly rise from low values to high

values, then lowers to mid values. It was also noted that there are three flat sections on widow shade

controller output graphs; the window shade closed position with output value of about 0.18, the fully

open position with output value of about 0.9, and the mid open position with output value of about

0.7. This confirmed that the window shade position fuzzy logic controller was operating in response

to the “if-then” control rules set out in table 3.1 in chapter 3.

The LEDs fuzzy logic controller output signal was also noted to follow the indoor illumination levels

but in a contrasting manner; as the indoor light intensity increases, the LEDs controller output signal

decreases. The indoor light intensity signal was noted to oscillate at particular outdoor light signal

levels. This indoor lighting oscillation behavior was as expected; the LEDs fuzzy logic controller

trying to meet the room’s light level requirements in the closed loop control signal circuit.

The test also confirmed that the LEDs’ current pulse width was increasing with the LEDs fuzzy logic

controller output signal level and vice-versa. The line graph 4.5 confirms that when the outdoor light

contribution is zero, the LEDs fuzzy logic controller output signal is at highest level and hence LEDs

output luminance level. When the window shade opening signal level is highest, the corresponding

LEDs fuzzy logic controller output signal level is, in contrast, the lowest. This is as was expected

and confirms that the design model was functional and effective in saving electric energy for lighting

a typical room when the outdoor daylight is available.

The fuzzy logic controllers do not provide the expected absolute lowest set signal values, i.e. 0, or the

highest set values, 1 for the window shade controller and 2 for the LEDs controller. This problem was

noted as weakness with fuzzy logic controllers, due to their approximation (defuzzificztion)

approach.


77

4.5 Effect of Different Level Values of the Red, Green and Blue Colour Portions

The objective of this test was to confirm the model’s ability to calculate ratio values of the primary

colour (red, green, and blue) levels, reproduce a signal that represents the whiteness of the selected

light (colour product) which is used as an input to the window shade position fuzzy logic controller.

It was expected that for white light signal value, the product of the colour ratios, would be about 1.

Coloured light signal values were expected to have colour ratio product of less than 0.8.

The other objective of this test was to confirm if the system model could use the primary colour ratios

to determine the respective RGB LEDs current pulse width. It was expected that for smallest colour

ratio, the respective colour LED current pulse width would also be the narrowest. The highest colour

ratio value was expected to be 1 and the respective colour LED current pulse width would be widest

and directly proportional to the controlling signal value from the LEDs fuzzy logic controller.

The colour ratio product was also expected to close the window shade if its signal value is less than

0.8. Contribution of the LEDs light to the room illumination levels was expected to be proportional to

the colour ratio product.

4.5.1 Results of testing effect of different level values of colour portions on indoor illumination

levels and the width of the LEDs’ current pulses.

The test was carried out using varying values of the three colour portions but at constant value of the

outdoor light intensity of 5, constant value of the window shade fuzzy logic controller output gain

factor of 10, constant zero value for rate of change of the indoor light intensity, enabled room

movement detector, and enabled room occupancy. The simulation model data values were as shown

in table 4.4. Graph set 4.7 compares the wave forms of indoor illumination level signal, and output of

the LED light intensity fuzzy logic controller, and the current pulse widths of each of the three red,

green and blue LEDs for various values of the input red/green/blue colour portions. Graph 4.8 is a

line graph showing the relationship of the various values of the red/green/blue colour portions with

their ratio product, “colour product”. Graph 4.9 is a line graph showing the relationship of the

red/green/blue portion value, colour ratio product signal and the window shade controller output and

the LEDs’ controller output signals. Graph 4.10 is also a line graph showing the relationship of the

various values of the red/green/blue colour portions and their respective signals for each colour


78

LED’s PWM generator. The results were obtained from the time based graphical representation of the

simulation on various signal level monitoring points in the simulation model. The results are in wave

forms of the various signals at specific monitoring points using the model scopes. The results are

presented in form of scopes’ screenshots. The wave forms are displayed in table format for column-

wise comparison purposes. The respective values extracted from the appropriate scopes were

tabulated and displayed in line graphs where the y-axis represents values and the x-axis represents the

colour product signal values.

Table 4.4: Constant values- Effect different signal levels of colour red, green, blue portions.

Outdoor

light

level

value

Red

colour

part

value

Green

colour

part

value

Blue

colour

part

value

Rate of

change

of

indoor

light

intensity

gain

factor

Movement

detector

value

Number

entering

room

Number

leaving

room

Window

shade

position

gain

factor

value

LED

light

control

sum

gain

factor

value

5 - - - 0 1 2 1 10 1


79

Graph set 4.7: Set of screen shots of respective scopes- Effect of different values of the red, green and blue colour portion signal levels.

Colour levels

Red=0:Green=1:Blue=1

Colour levels

Red=0.5:Green=0.5:Blue=0.5

Colour levels


Colour levels

Red=1:Green=0.5:Blue=0.25

Indoor light

level

feedback

signal

Output of

the LED

light

intensity

fuzzy logic

controller

Red LED current

pulses

Green

LED current

pulses

Blue LED current

pulses


0.8

1.0

0.7

0.7

0.8

1.0

0.7

0.8

1.0

0

0 0 0

3.5

4.5

1.0

1.3

1.8

10

5

0.2

1.0

1.8

4

0.0

1.2

1.8

3.5

1

1.1

1.4

1.8

79

.


80

Colour levels

Red=3:Green=1.5:Blue=0.75

Colour levels


Colour levels

Red=1:Green=0.5:Blue=0

Colour levels

Red=0.05:Green=0.025:Blue=0.015

Indoor light

level

feedback

signal

Output of

the LED

light

intensity

fuzzy logic

controller

Red LED current

pulses

Green

LED current

pulses

Blue LED current

pulses


0 0 0 0

0.7

0.7

0.7

1.0

1.0

1.0

0.8

0.8

0.8

3.0

4.0

1.1

1.8 1.8 1.9 1.8

1.3

1.1

1.3

3.0

4.0

3.0

1.2

1.4

3.5

1.1

1.3

80

.


81

Graph 4.8: Line graph – Effect of Red, Green, and Blue values on colour product signal

Graph 4.9: Line graph – Effect of white light signal on window shade controller output


82

Graph 4.10: Line graph –Effect of Red, Green, and Blue colour portions on respective PWM

generator input signal level

‘Colour product’ is a term used to represent the product (multiplication) of the three colour portion

calculated ratios. Colour product signal level value is a measure of the whiteness of the LEDs output

light. For colour product signal level value of 0.80 to 1, the LEDs’output light is considered white

and the window shade may open depending on the outdoor light intensity.

Graph set 4.7 of screenshots show that even though the outdoor light intensity is constant at signal

level value of 5, the window shade opening position is determined by the colour product signal level

value. For colour product signal value of about 0, the indoor illuminance is low, about 3.5, and the

LEDs fuzzy logic controller output signal value is highest at about 1.35. The LEDs’ respective

current pulse width is most narrow for the corresponding colour portion with almost zero value but

widest for the colour portion with largest value. This demonstrates that each colour LED’s average

power output is proportional to fraction of its respective colour portion level value.

Graph 4.8 is a line graph, which show that when the three red/green/blue colour portion level values

are far apart their fraction product tends to 0, but when the values are close their fraction product


83

tends to value of 1. Line graph 4.9 show that when the colour product values are low, from 0 to 0.6,

the window shade is closed. But colour product signal values are from 0.8 to 1, the window shade is

open. This demonstrates that the lighting system model window shade opens only when the decoded

colour signal is confirmed to be from a white light.

Graph 4.10 shows the relationship of the three colour portions and their corresponding colour signals

for controlling the respective LEDs’ PWM pulse generators. This graph shows that the respective

LEDs’ PWM pulse generator input signal is fraction of the LEDs fuzzy logic controller signal, which

is less by a constant value of 1 but of a pattern similar to that of the colour decoder signal level

values. Negative PWM generator input values, represent narrower LED’s current pulse widths.

4.5.2 Discussion on effect of different level values of colour portions on indoor illumination

levels and the width of the LEDs’ current pulses.

The test confirmed that the model is able to calculate ratio values of the primary colours (red, green,

and blue), and the colour ratio product signal used as an input to the window shade position fuzzy

logic controller. The model could recognize required LEDs’ output white light and open the window

shade for the daylight to enter in the room. White light signal was confirmed to have colour ratio

product value of more than 0.8.

The test also confirmed that model design could use the primary colour ratios to determine the

respective RGB LEDs current pulse width, and hence the LEDs’ output light colour. The smaller the

colour ratio, the narrower the respective colour LED current pulse width would be. The smaller the

LEDs current pulse width, the less the intensity of light (luminance) from the respective LED. Hence

the colour ratio signal value for each respective primary colours determine the overall colour of the

combined light from the three RGB LEDs.

The system design model demonstrated that it can effectively control colour of light produced by the

RGB LEDs in a typical room, and at the same time allow daylight to be used in the room if only

white light is required, saving electric lighting energy. The lighting system design simulation, also

demonstrated that it is sensitive to the room user’s needs for coloured light by closing the window

shades, even during day time, and prevent interference of the room’s required lighting colour from

the more bright outdoor white daylight.


84

4.6 Cause and effect of absence of Colour Decoder, Movement Detector, and

Room Occupancy Signals.

The objective of this test was to check and confirm if the system model could actually switch off the

LEDs by means of a signal level value from either the colour decoder (selector), or the movement

(motion) detector, or the room occupancy counter. This test was based on signal enabler switches put

in series with the output signal circuit from the LEDs fuzzy logic controller as shown in figure 3.35, a

section of the lighting system model in chapter 3. This test was also to confirm that absence of the

respective sensor output signal is caused by lack of activation on the sensor input for the colour

decoder, and the movement detector. The room occupancy counter output signal is determined by

arithmetic difference of the entry and exit counter readings.

It was expected that if a signal level value from either of the three sensors; colour decoder, movement

detector, and the room occupancy, is less than 0.5 (a set value for activating each enabler switch),

then the LEDs fuzzy logic controller output signal could automatically be blocked and the affected

enabler-switch output signal value turned to zero and hence switch off the LEDs.

4.6.1 Results of testing cause and effect of absence of the colour decoder, movement detector,

and room occupancy signals.

The test was carried out using the following parameters maintained constant: outdoor light intensity

of 5, window shade fuzzy logic controller output gain factor of 10, rate of change of the indoor light

intensity at 0. The simulation test was done for the colour decoder, the movement detector, and the

room occupancy. For the colour decoder, the three primary colour portion inputs were each given

zero value, the movement detector input given 1, and the room occupancy counter inputs given 2 and

1 for entrance and exit respectively. The colour signal enabler switch output was expected to have

zero value and the LEDs’ output current pulse width to be very narrow. Same test was repeated for

movement detection and the room occupancy respectively. Graph set 4.11 compares the wave forms

of indoor illumination level signal, and output of the LED light intensity fuzzy logic controller, and

the current pulses for the red LED (as a representative of the three LEDs). The screenshot waveforms

for the red LED’s current pulses are compared for the three tests.


85

Graph set 4.11: Set of screenshots of the respective scopes – Effect of zero values of colour portions (cs) (colour white signal), movement

detector signal (Md), and room occupancy signal (Ro).


Room occupancy =1

Movement detection signal=1

(Testing effect of no colour signal)


Room occupancy =1


(Testing effect of no movement

detection signal)


Room occupancy =0


(Testing effect of no room occupancy

signal)

Indoor light

level

feedback

signal

Output of

the LED

light

intensity

fuzzy logic

controller Output -2

of the LED

light

intensity

fuzzy logic

controller Red/Green/

Blue LED

current

pulses

0.4

0.0

1.0mSec 1.0mSec 1.0mSec

0

-1.0

+1.0

1.0

1.9

0 0

0

1.0

0

9.0

0.4 0.4

2.0 2.0

9.0

0 0

0 0

85

.

+1.0 +1.0

-1.0 -1.0

1.0 1.0

0.7 0.7


86

The waveforms displayed in graph set 4.11 show that the signal output (output 2) of the LEDs’ output

fuzzy logic controller, after the enabler switches, is zero for the three tests.

It was noted that when the three colour portion values are zero, then the lighting system model does

not give any signal at the white light signal input to the window shade position controller as well as to

the LEDs controller. The room feedback signal disappears, and the LED’s current pulses do not

occur. However, when the movement detector or the room occupancy signals is zero, the system

functions well with zero value for each colour signal to the PWM pulse generator and the LED’s

current pulses occur though with very thin width.

4.6.2 Discussion on effect of zero values of the colour decoder signal, movement detector

signal, and room occupancy signal.

The expectations of this test were partially met. The system model could actually switch off the LEDs

by means of a signal value level from the colour decoder, or from the movement detector, or from the

room occupancy counter. However, the colour decoder signal was noted to disturb the system’s

signal flows. This could be associated with the system design defect. The enabler switch for the

colour decoder signal may not have been necessary. The LEDs control signal distribution through the

colour ratio multiplier shown in figure 3.37 in chapter 3, also produces zero signal value when the

respective colour ratio signal value is zero and this could cause mathematical simulation problem.

It was also noted that the respective LED’s current pulse width could not be exactly zero as expected

for -1 input to the PWM generator. This problem could be associated with the fact that the PWM

pulse generators (PWM red, PWM green and PWM blue) shown in figure 3.38 of chapter 3 are not

ideal; cannot give zero width pulses.

4.7 Effect of Too High, Moderate, Low and Negative Values of Outdoor Light

The objective of this test was to check and confirm if signal from the outdoor light intensity sensor

with values outside the set range of the window shade fuzzy logic controller membership function

configuration does really affect the system’s controllability.


87

The output signal from the window shade position fuzzy logic controller was expected to be of a

constant value, same as one before outdoor light signal level had changed, without changing with

change of the outdoor light signal value that lies outside the preset controller membership function

range shown in figures 3.18 and 3.19 in chapter 3.

4.7.1 Results of testing effect of too high, moderate, very low, and negative values of outdoor

light intensity on window shade controller output signal, indoor illumination levels, and

the LEDs’ controller output.

The test was carried out using a 200Hz sinusoidal signal generator as the outdoor light intensity

signal source. The other system parameters were maintained constant with the values for each

red/green/blue input colour portion levels at 1, value of the window shade fuzzy logic controller

output gain factor of 10, zero value for rate of change of the indoor light intensity, enabled room

movement detector, and enabled room occupancy. The 200Hz sinusoidal signal was used for various

amplitudes. The test result graph set 4.12, graph set 4.13, and graph set 4.14 were used for comparing

the waveforms of the outdoor light intensity signal, the window shade position controller output

signal, indoor illumination level signal, and the LEDs’ light intensity fuzzy logic controller output

signal.

The results were obtained from the time based graphical representation of the simulation on various

specific scopes of the MATLAB Simulink simulation model. The results are in waveforms of the

various signals at specific monitoring points using the model scopes. The results are presented in

form of the scopes’ screenshots. The waveforms are displayed in table for column-wise comparison

purposes. Graph set 4.12 compares effect of outdoor light intensity of sinusoidal signal amplitude of

10 with that of 20. Graph set 4.13 compares signal amplitude of 10 with that of 7, while graph set

4.14 compares outdoor light signal amplitude of 10 with that of 3.


88

Graph set 4.12: Set of screen shots- Effect of too high outdoor light intensity signal – Positive and negative input signals

Outdoor Light intensity sinusoidal signal with

frequency of 200Hz and amplitude of 10



Outdoor light

- Input signal.

Amplitude

=10 and 20

for

comparison

Window

shade

position fuzzy

logic

controller

output

Indoor (room)

light level

Indoor LED

light level

fuzzy logic

controller

output

0 0 2.5mSec 2.5mSec

5 5

0.0

1.0

1.8

0.0

1.0

1.8

0 0

10

6

10

6

0 0

0.9 0.9

0.5 0.5

0 0

10 20

-10 -20

88

.


89

Graph set 4.13: Set of screen shots – Effect of moderate outdoor light intensity signal values – Positive and negative signals





Outdoor light

- Input signal.

Amplitude

=10 and 7 for

comparison

Window

shade position

fuzzy logic

controller

output

Indoor (room)

light level

Indoor LED

light level

fuzzy logic

controller

output

0 0 2.5mSec 2.5mSec 5 5

0 0

0.0 0.0

10

6

10

5

0.0 0.0

1.0

1.8

1.0

1.8

0.5 0.5

0.9 0.9

0 0

10

-10

7

-7

89

.


90

Graph set 4.14: Set of screenshots – Effect of low outdoor light intensity signal values- Positive and negative signals





Outdoor light

- Input signal.

Amplitude

=10 and 3 for

comparison

Window

shade position

fuzzy logic

controller

output

Indoor (room)

light level

Indoor LED

light level

fuzzy logic

controller

output

0 2.5mSec 2.5mSec 5 5

0.0

0.0

0 0

0.0

0 0.0

1.0

1.8

1.0

1.8

10

6

7

5

0.9

0.5

0.5

0.3

0 0

10

-10

3

-3

90

.


91

The wave forms displayed in graph set 4.12, where effect of outdoor light sinusoidal signal with

amplitude of 10 is compared with that of 20, show that the window shade position controller output

signal is low at low outdoor light input signal and rises to maximum value of 0.9 as the outdoor light

signal passes through the 4 to 6 level values then falls to mid position value of 0.5 for any other

values of outdoor light intensity beyond 9. This observation is true for both signals with amplitude of

10 and that of 20. The indoor illumination level values follow the curve form of the window shade

position controller output. The LEDs’ fuzzy logic controller output signal is observed to effect

reduction or increase of the LEDs average power output depending on the indoor illumination levels.

The wave forms displayed in graph set 4.13, where effect of sinusoidal signal with amplitude of 10 is

compared with that of 7, show that the window shade position controller output signal is low at low

outdoor light input signal and rises to maximum value of 0.9 as the outdoor light signal passes

through the 4 to 6 level values then falls to value of about 0.7 for the values of outdoor light intensity

to maximum of 7, which is the signal amplitude. This signal with amplitude of 7 does not drive the

window shade position fuzzy logic controller to maximum input range, hence the controller output

signal does not reach the mid position of 0.5. The indoor illumination level values follow the curve

form of the window shade position controller output. The LEDs’ controller output signal is observed

to effect reduction or increase of the LEDs average power output depending on the indoor

illumination levels.

The waveforms displayed in graph set 4.14, where effect of sinusoidal signal with amplitude of 10 is

compared with that of 3, show that the window shade position controller output signal is lowest, 0.05,

at low outdoor light input signal and rises to maximum value of only 0.5 as the outdoor light signal

passes through the 2 to 3 level values then falls again to its lowest value of about 0.05, as the values

of outdoor light intensity falls back to 0. This outdoor light signal with amplitude of 3 does not drive

the window shade position fuzzy logic controller to maximum input range. The indoor illumination

level values follow the curve form of the window shade position controller output. The LEDs’ fuzzy

logic controller output signal level is observed to rise and fall depending on the indoor illumination

levels.

The values of the outdoor light intensity signal are observed to be negative after 2.5 milliseconds of

the simulation start. The waveforms displayed in graph sets show that the window shade position


92

controller output signal is always positive, and rise to mid-point constant value of 0.5, as the outdoor

light input signal level falls from zero to maximum negative value for each test input signal

amplitude of 7, 10, and 20. But for test input signal with amplitude of 3, the window shade position

controller output signal rises to a maximum value of 0.055.

4.7.2 Discussion on effect of too high, moderate, very low, and negative values of outdoor light

intensity on window shade controller output signal, indoor illumination levels, and the

LEDs’ controller output.

The objective of this test was fully met. The signal from the outdoor light intensity sensor with values

outside the set range of the window shade fuzzy logic controller membership function configuration

does really affect the lighting system’s controllability. When the output signal for controlling the

window shade position is affected, the indoor light levels and the feedback signal to the LEDs fuzzy

logic controller are also affected.

The output signal from the window shade position fuzzy logic controller was of a constant value,

mid-point value of 0.5, for almost all outdoor light signal value that lies outside the preset controller

membership function range. However, for outdoor light signals with values which are just at the

edge, though outside the membership function range, the fuzzy logic controller output is not constant.

This phenomenon could be attributed to the shape of the end-side controller membership function

plots.

The window shade position fuzzy logic controller does not correctly respond to too high or negative

outdoor light intensity signal values. The test also confirms that LEDs fuzzy logic controller output

signal level rises and falls in contrasting rhythm of the indoor illumination level values, which follow

the curve form of the window shade position fuzzy logic controller output. The LEDs fuzzy logic

controller output signal effect reduction or increase of the LEDs average power output depending on

the indoor illumination levels.

The designed lighting system should not be used with lighting intensity signal level values that lie

outside the respective preset fuzzy logic controller membership function ranges.


93

4.8 Effect of Changing Shape of the Membership Functions

The objective of this test was to check and confirm if the system design control can be improved by

changing the respective fuzzy logic controller membership function plot shape. The test was to

compare and deduce which of the two shapes, triangular and trapezium, is most suitable for stability

of the LED lighting system.

It was expected that the triangular shaped membership functions to be more stable due to their

narrower top plot shape when compared with the trapezium shapes.

4.8.1 Results of testing effect of changing the plot shape of the fuzzy logic controllers’

membership functions from triangular shape to trapezium shape.





movement detector, and enabled room occupancy. The 200Hz sinusoidal signal amplitude was 10.

The test result graph set 4.15 was used for comparing the wave forms of the outdoor light intensity

signal, the window shade position controller output signal, indoor illumination level signal, and the

LEDs fuzzy logic controller output signal. The first six rows of the table compare the shapes of the

membership function plots for the window shade position and the LEDs’ Fuzzy Logic controllers.




form of screenshots. The waveforms are displayed in table for column-wise comparison purposes.

Graph set 4.15 compares effect of changing the controller’s membership function plot shapes from

that of triangular to trapezium.


94

Graph set 4.15: Set of screenshots – Effect of changing from triangular to trapezium shapes of membership functions.

Triangular membership shape Trapezium membership shape

Membership function:

Input-

Room light intensity


Input-

Rate of change of room

light intensity


Output-

LEDs output


Input-

Outdoor light intensity


Input-

Colour product level

94

.

0 0 0.5 1 0.5 1 0

0.5

1.0 1.0

0

0.5

Not White White Not White White

0 5 10 0 5 10

0

0.5

1.0

0

0.5

1.0 Low Mid Bright

Low Mid Bright

0 1 2 0 1 2 0

0.5

1.0

0

0.5

1.0 Max Low Max Low Low Low Mid Mid High High Max High Max High

-1 0 +1 -1 0 +1 0

0.5

1.0

0

0.5

1.0 Negative Zero Positive Negative

Zero Positive

0 10 5 0 5 10 0

0.5

1.0

0

0.5

1.0 Very Dim Dim Bright Mid V. Bright Very Dim Dim Mid Bright V. Bright


95


Output-


Outdoor light - Input

signal. Amplitude =10


fuzzy logic controller

output

Indoor (room) light level

LEDs light level fuzzy

logic controller output

0 0 2.5mSec 2.5mSec 5 5 0.0

1.0

1.8

0

10

6

0.0

0.5

0.9

10 10

0 0

-10 -10

95

.

Mid Closed Open

0 0.5 1 0

0.5

1.0 Closed Mid High

0 0

0.5

0.5

1.0

1 Window shade position Window shade position


96

The wave forms displayed in graph set 4.15, where sinusoidal signal with amplitude of 10 is used for

the outdoor light intensity, show that the window shade position controller output signal is low at low

outdoor light input signal and rises to maximum value of 0.9 as the outdoor light signal passes

through the 4 to 6 level values then falls to mid position value of 0.5 for any other values of outdoor

light intensity beyond 9. It is observed that the shapes of the two sets of wave forms are similar.

However, the duration at which the window shade position controller output signal is at maximum is

longer in the trapezium membership function than with triangular shape. Another noted difference in

the two sets is the oscillation transient duration of the indoor illumination levels and the LEDs’

controller output signal. The triangular membership function set of output signal transient oscillations

settle faster than those by the trapezium membership functions.

4.8.2 Discussion on effect of changing the plot shape of the fuzzy logic controllers’

membership functions from triangular shape to trapezium shape.

The objective of this test was fully met. The system design control can be improved by changing the

respective fuzzy logic controller membership function plot shape. The test confirmed that the

narrower tipped membership function plot are more stable and more accurate than their counterparts

with flat tops. Triangular shaped membership functions are better than trapezium ones for stable

fuzzy logic control systems.

4.9 Effect of Shifting Plot Position of the Mid Membership Functions.

The objective of this test was to check and confirm if shifting the plot position of the mid

membership function of the fuzzy logic controllers, the system settles in new values.

It was expected that when the mid membership function plot for the window shade position fuzzy

logic controller is shifted to higher value in the scale range, the controller output signal also settles in

a higher value level.


97

4.9.1 Results of testing effect of shifting position of the outputs’ mid membership functions for

the fuzzy logic controllers.





movement detector, and enabled room occupancy. The 200Hz sinusoidal signal amplitude was 10.

The test result graph set 4.16 was used for comparing the wave forms of the outdoor light intensity

signal, the window shade position controller output signal, indoor illumination level signal, and the

LEDs fuzzy logic controller output signal. The first two rows of the table compare the positions of

the outputs’ mid membership functions for the window shade position and the LEDs’ Fuzzy Logic

controllers.




form of screenshots. The waveforms are displayed in table for column-wise comparison purposes.

Graph set 4.16 compares effect of shifting the outputs’ mid membership function for both fuzzy logic

controllers.


98

Graph set 4.16: Set of screenshots – Effect of shifting the fuzzy controller output middle membership function plots.

Mid membership function plot in middle of the graph

range

Mid membership function plot shifted to right hand-side

of middle position of the graph range

Membership

function:

Output-

LEDs output

level

Membership

function:

Output-

Window

shade position Outdoor light -

Input signal.

Amplitude =10

Window shade

position fuzzy

logic controller

output Indoor (room)

light level

LED light level

fuzzy logic

controller output

0 0

2.5mSec 2.5mSec 5 5 0.0

0.0

0 0

10 10

-10 -10

0.9

0.5

1.8

1.0

98

.

Mx Low Low Mid High

10

0

6

0.9

0.0

0.5

9

5

0

1.8

1.0

0.0

Max High Mx Low Low Mid High Max High

0 1 2 0

0.5

1.0

0 1 2 0

0.5

1.0

Closed Mid Open Closed Mid Open

0 0.5 1 0 0.5 1 0

0.5

1.0

0

0.5

1.0

Wind shade position Window shade position

LEDs Output LEDs Output


99

The waveforms displayed in graph set 4.16, where sinusoidal signal with amplitude of 10 is used for

the outdoor light intensity, show that the window shade position controller output signal is lowest at

low outdoor light input signal for both cases. The window shade position controller output signal

rises to maximum value of 0.9 as the outdoor light signal passes through the 4 to 6 level values then

falls to mid position value of 0.5, for case mid in middle, but 0.7 for case mid shifted to right hand

side. It is observed that the shape of the two sets of wave forms are similar, apart from the higher

values of the window shade position controller output and the indoor illumination level values when

the outdoor light intensity is at high values, from 8 to 10. The LEDs’ controller output signal, when

the mid is shifted, is noted to be also at lower level in response to the indoor illumination values.

Shifting the fuzzy logic controller output mid membership function from middle of the plot to right

hand-side has the effect of shifting higher the value of the settled indoor illumination level.

4.9.2 Discussion on effect of shifting position of the outputs’ mid membership functions for

the fuzzy logic controllers.

As was expected, when the mid membership function plot for the window shade position fuzzy logic

controller was shifted to higher value in the scale range, the controller output signal settles in a higher

value level.

This test confirmed that shifting the plot position of the mid membership function of fuzzy logic

controllers the system control signals settle in new value levels. Controlling the window shade

position and the LEDs output light to provide the desired room illumination levels could also be

achieved through shifting the positions of the specific membership functions for each respective

fuzzy logic controllers. Shifting the positions of the membership functions is a one method for fine

tuning the designed lighting system controls.


100

CHAPTER 5 - CONCLUSION AND RECOMMENDATIONS

5.1 Introduction

The study was set out to explore and design an intelligent lighting system based on fuzzy logic

controller that uses multi-colour LEDs to produce light of the required luminance level and

appropriate colour in a typical room space considering energy efficiency requirements. This study

was based on the necessity to optimize utilization of the new generation of LEDs and the natural

daylight in both homes and work places as a strategy to save lighting related energy costs, and at the

same time improve comfort of living and working in such places. The study sought to answer these

questions:

1. How can the output of an outdoor lighting level sensor be configured and processed to

automatically control a room’s window shade position?

2. How can the output of an indoor lighting level sensor be configured and processed to intelligently

control LEDs’ luminance and maintain the required room’s illumination levels?

3. How can the output of an occupant detector that counts the number persons in a room be

configured and processed to dim off the LEDs lights if the room is not occupied?

4. How can the output of a movement detector be configured and processed to dim off the LEDs if a

movement in the room is not detected?

5. How can the output of a colour decoder/sensor be processed to accurately determine the multi-

colour LEDs’ output light colour and at the same time close the room’s window shades if the

output light is not white?

6. Can the integrated system of addressable sensors, window shade, fuzzy logic controllers, and

multi-colour LEDs provide a typical room with light of appropriate colour at required intensity

levels and still be efficient in electrical energy usage?

The empirical findings of the study are synthesized and explained in this chapter. Also explained in

this chapter is the theoretical implication of the study and the recommendations for future works.


101

5.2 Empirical Findings

This section will synthesize the empirical findings to answer the study’s research questions.

1. How can the output of an outdoor lighting level sensor be configured and processed to

automatically control a room’s window shade position?

In chapter 4 section 4.4, on effects of outdoor light intensity, the window shade position controller

output signal smoothly rise from low values to high values, then lowers to mid values. It was also

noted that there are three flat sections on widow shade controller output graphs; the window shade

closed position with output value of about 0.18, the fully open position with output value of about

0.9, and the mid open position with output value of about 0.7. This confirmed that the window shade

position fuzzy logic controller was operating in response to the fuzzy logic control table of

operational rules. The room’s window shade position could automatically remain closed for low

outdoor light levels and open to maximum position once the outdoor light levels start rising, then

close to middle position when the outdoor daylight is very bright.

The outdoor lighting level signal, through the Fuzzy Logic Controller, automatically controlled the

room’s window shade opening position in a manner similar to human reasoning basing it

controllability on the “IF-THEN” control rules. The lighting system design was able to automatically

harvest daylight to suit the desired room illumination level requirements.

The simulation model demonstrated how a fuzzy logic controller can be used to process an outdoor

light level signal and control a typical room’s window shade position, heuristically harvesting

daylight for energy efficient illumination of the room.

2. How can the output of an indoor lighting level sensor be configured and processed to

intelligently control LEDs’ luminance and maintain the required room’s illumination levels?

Section 4.2 of chapter 4 on effects of the rate of change of the indoor light intensity, the indoor light

level sensor output signal is demonstrated to affect the LED’s current pulse width via the fuzzy logic

controller. The results also confirm that room illuminance level stability is affected by the way the

indoor light sensor signal is configured for use in the LEDs fuzzy logic controller. However, the


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system stability does not depend on magnitude of the rate of change signal but on some critical values

of the rate of change. The system is seen to be stable even without input from the rate of change of

the indoor illumination levels. This does not mean that the signal for rate of change of the indoor light

intensity is not required, but it implies that the fuzzy logic controller settings require tuning.

The room’s illumination was maintained above a certain level irrespective of absence of the outdoor

light contribution. The LEDs’ output light depends on illumination levels in the room and

configuration of its fuzzy logic membership functions. When there is no outdoor light, the room is

illuminated by the LEDs only. When the outdoor light into the room is high enough to the required

levels, the LEDs’ output luminance is at the lowest level possible, thus saving on electric energy.

However, the simulation results showed that the lowest level of the LEDs output light is not zero as

expected. This problem was associated with fuzzy logics’ weakness of approximation. Lowest value

of the fuzzy logic controller output is not zero but a number with approximate value of zero.

The simulation model design proved that an indoor light intensity level sensor signal could be

processed through a fuzzy logic controller to intelligently control a room’s LEDs light intensity and

maintain the required illumination levels.

3. How can the output of an occupant detector that counts the number persons in a room be

configured and processed to dim off the LEDs lights if the room is not occupied? Also, how

can the output of a movement detector be configured and processed to dim off the LEDs if a

movement in the room is not detected?

The simulation model design tests, in chapter 4 section 4.6 on cause and effect of absence of colour

decoder, movement detector, and room occupancy signals, demonstrated that the lighting system

LEDs could be dimmed off by a signal from either the room occupancy counter, or the movement

detector, or the colour decoder. The system model incorporated three enabler signal switches at the

output of the LEDs fuzzy logic controller. However, the use of enabler switch for zero value colour

decoder signal was found to cause disruption in the system signal flow. The simulation model did not

require colour decoder signal enabler switch because the same zero value signal is duplicated after

the multiplier blocks.


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4. How can the output of a colour decoder/sensor be processed to accurately determine the

multi-colour LEDs’ output light colour and at the same time close the room’s window shades

if the output light is not white?

Chapter 4 section 4.5 on effects of different level values of colour portions, the system was found

able to calculate the respective primary colour portion signal level ratios, which are used apportioning

the average power for each respective red, green, and blue colour LED. The model simulated how the

calculated colour portion ratios could be used to differentiate a white light from coloured one and

then close the room’s window shades if required light is not white. The model design demonstrated

how an output of a colour sensor (colour decoder) with three distinct signal lines, for each specific

primary colour, can be processed to accurately determine a multi-colour LEDs’ lamp output light

colour.

5. Can the integrated system of addressable digital sensors, window shade, fuzzy logic

controllers, and multi-colour LEDs provide a typical room with light of appropriate colour at

required intensity levels and still be efficient in electrical energy usage?

Section 4.7 on effect of too high, moderate, low and negative values of outdoor light signals, section

4.8 on effect of changing shape of the membership functions plots, and section 4.9 on effect of

shifting position of the outputs’ mid membership functions for the fuzzy logic controllers, illustrated

that the appropriate window shade position for any daylight intensity level, and the LEDs’ output

luminance can easily be adjusted by simply shifting the position of the membership functions in the

plots, or adding more sets of membership functions, or changing the shape, or doing a combination of

the three. The lighting system operates well when the outdoor and indoor lighting signal level values

are within the respective fuzzy logic controller membership function set range. If the input signal

values are outside the membership function range, the controller output signal is fixed in the mid-

range and is invariant to the input changes.

The lighting system is more stable and sensitive to signal inputs if the fuzzy logic controller member

ship functions are of sharp-tipped shapes than those with flat topped shapes.

The research simulation model fulfilled the main objective of designing an intelligent lighting system

based on fuzzy logic controller that uses multi-colour LEDs to produce light of the required


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luminance and appropriate colour in a room space that considers energy efficiency requirements. The

integrated system of digital sensors, fuzzy logic controllers, and the multi-colour LEDs is energy

efficient and intelligent, ensuring that the electric energy for the LEDs is used only when the outdoor

daylight is not required and only when the room is occupied and the user is not asleep. The lighting

system is sensitive to colour needs; the window shade closes when the room’s required light colour is

not white.

5.3 Theoretical Implication

The theoretical cases for lighting systems with fuzzy logic controllers need to be revisited in order to

further understand the dynamics of lighting systems and how to the designs can be made more

beneficial to mankind.

In a paper by S.D. Panjaitan [1], fuzzy logic scheme is proposed as a basis to control lighting system

in building zone where the fluorescent lamps are the controlled objects. The proposed lighting design

procedure determines the maximum number of lamps that should be used according to the room

function. Then the proposed controller is activated and deactivated according to information from

occupancy sensors, which gives information about room occupancy. This method saves electrical

energy for lighting, but may not be most appropriate for a small room which has a few number of

lighting lamps.

The paper by M.A.A. Saleh [29] also describes a fuzzy controller that controls the number of lamps

lit using the number of people inside the room and the required illuminance as control inputs. This

method also is capable of reducing the electrical energy, but not suitable for smaller rooms with only

one or two lamps.

M. T. Lah [18] proposed a modern approach to control the inside illuminance with fully automated

fuzzy system for adjusting shades, which responds constantly to the changes in the available solar

radiation, which makes decisions as it follows the human thinking process. The control algorithm

contains a cascade control with fuzzy controller as the main and conventional PID-(proportional-

integral-derivative) controller as auxiliary controller. This approach was very good for harvesting the

daylight. This design was limited only to daylight harvesting but does not consider artificial lighting

energy requirements.


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The researched design lighting system with fuzzy logic controller operate in a manner similar to

human reasoning. Fuzzy logic controllers are suitable for lighting systems due to the none-linearity of

lighting brightness and different lighting requirements for different persons.

This research study proposes a design of lighting system which combines the use of the high efficacy

LEDs, the daylight harvesting techniques, and production of coloured light for more comfort and

convenience of the space user. The lighting system was designed with need for saving electric

energy in mind. Of major concern in the design was reduction of human intervention in switching off

or dimming the electric lamps. This reduced human intervention ensurs that energy saving is part of

the lighting system. The system design considers the colour decoder to be of a flexible type which is

sensitive to the room users’ need for coloured light and the associated emotional soothing effects.

A fuzzy logic controller has been found be a system controller that is easily configured to operate

automatically and in a manner similar to human reasoning, displaying artificial intelligence

capabilities.

5.4 Recommendations for further research works

The research work was limited to computer model simulation, and therefore, further research works

in implementing the system is recommended. The proposed physical implementation scheme may be

used in realizing the multi-coloured lighting system.

The system need to be built up with real hardware and confirm control of the actual required room

space illumination and test the desirable lighting colour. The fuzzy logic membership functions could

be tuned-up to confirm functionality of the ‘rate of change of the indoor light intensity’ signal in

stability of the lighting system. The colour decoder requires further checks for accuracy and

suitability.


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APPENDICES

Appendix 1: Effects of Window shade position Fuzzy logic Controller output gain factor

Table of data for window shade gain factor

Window Shade

position Fuzzy

controller

output Gain

Indoor

Light

Intensity

LED Fuzzy

Controller

output (1)

1 0.000 3.760 1.253

2 3.760 5.775 0.834

3 5.000 6.495 0.716

4 7.500 8.000 0.489

5 10.000 9.575 0.292

6 11.000 10.000 0.160

7 11.200 10.230 0.162

8 11.480 10.480 0.165

9 11.490 13.000 1.000

10 11.495 13.100 1.000

11 12.000 13.440 1.000

12 15.000 16.000 1.000

Line Graph


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Appendix 2: Effects of outdoor light intensity

Table of data

Outdoor Light

Intensity

Window Shade

position Fuzzy

controller output

Indoor Light

Intensity

LED Fuzzy

Controller

output (1)

1 0.00 0.130 4.680 1.870

2 1.00 0.138 4.735 1.076

3 2.00 0.152 4.838 1.053

4 2.50 0.380 6.400 0.730

5 3.00 0.550 7.458 0.505

6 3.95 0.839 9.800 0.20

7 4.00 0.859 9.950 0.170

8 5.00 0.870 10.050 0.160

9 6.00 0.857 9.940 0.170

10 7.00 0.608 8.113 0.483

11 8.00 0.500 7.220 0.573

12 9.00 0.500 7.2198 0.573

13 11.00 0.500 7.2193 0.573

14 12.00 0.500 7.220 0.574

Line graph


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Appendix 3: Effects of Colour Product (product of the primary colour’s ratios)

Table of data

Colour Product

LED Fuzzy

Controller output (1)

Window Shade

position Fuzzy

controller output

1 0.000 1.412 0.2000

2 0.000 1.2428 0.2004

3 0.120 1.2934 0.2004

4 0.222 1.2429 0.2004

5 0.375 1.1991 0.2004

6 0.375 1.2000 0.2004

7 0.450 1.1850 0.2004

8 0.450 1.1850 0.2004

9 0.500 1.1852 0.2004

10 0.630 1.1852 0.2004

11 0.720 0.9658 0.3150

12 0.810 0.4080 0.7650

13 0.855 0.4060 0.7681

14 0.862 0.4055 0.7680

15 0.898 0.4040 0.7681

16 0.903 0.4038 0.7680

17 0.942 0.4021 0.7681

18 0.947 0.40155 0.7681

19 0.948 0.40155 0.7681

20 1.000 0.40155 0.7681

21 1.000 0.40155 0.7681

Line graph


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Appendix 4: Effects of different portion level values of the primary colours

Table of data

Red

Colour

Portion

Green

Colour

Portion

Blue

Colour

Portion

Colour

Product

signal

LED Red

colour

signal

LED Green

colour

signal

LED Blue

colour

signal

1 1 0 0 0.00 0.412 -1 -1

2 1 1 0 0.00 0.2428 0.2428 -1

3 1 0.3 0.4 0.12 0.2934 -0.612 -0.483

4 3 2 1 0.22 0.243 -0.1715 -0.5857

5 0.5 0.75 1 0.38 -0.4005 -0.1007 0.2

6 1 0.75 0.5 0.38 0.2 -0.101 -0.4004

7 0.5 0.9 1 0.45 -0.4074 0.0667 0.1852

8 1 0.9 0.5 0.45 0.1852 0.0667 -0.4074

9 1 1 0.5 0.50 0.185 0.185 -0.408

10 1 0.9 0.7 0.63 0.1854 0.0667 -0.1703

11 1 0.9 0.8 0.72 -0.325 -0.1295 -0.2262

12 1 0.9 0.9 0.81 -0.592 -0.328 -0.633

13 1 0.95 0.9 0.86 -0.95 -0.6143 -0.6345

14 2 1.9 2.1 0.86 -0.614 -0.632 -0.5945

15 0.9 0.95 0.9 0.90 -0.6174 -0.5952 -0.6175

16 1 0.95 0.95 0.90 -0.596 -0.616 -0.6164

17 0.8 0.85 0.85 0.94 -0.6216 -0.5979 -0.5979

18 0.95 0.95 0.9 0.95 -0.5982 -0.5985 -0.6193

19 0.9 0.95 0.95 0.95 -0.6174 -0.5982 -0.5982

Line graph

Intelligent multi-coloured lighting system design with fuzzy logic controller

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